This is archival data. This system was retired as of August 21, 2023 and the information on this page has not been updated since then. For current information, visit catalogs.umn.edu.
Requirements for this program are current for
Spring 2023
Required credits to graduate with this
degree: 124
Required credits within the major: 104
Degree: Bachelor of Computer Engineering
The mission of the computer engineering program is to educate students in core topics, as well as in a broad set of specialties of computer engineering; to impart students with professional attributes that characterize a well-schooled engineer and citizen; and to provide students with opportunities for research experience in one of the leading computer engineering centers of scholarship.
The field of computer engineering resulted from the tremendous development of computers and, in particular, the evolution of microprocessors. The design process for almost every electronic system includes the specification and development of the control program for the system's microprocessor. A particular computer engineering job can be more closely related to hardware or software, to functional design or detailed design. The Bachelor of Computer Engineering degree provides the background necessary for persons, with continuing study, to work in many computer engineering subfields. The bachelor's degree itself does not, however, provide highly specialized knowledge in any particular subfield.
The program is accredited by the Engineering Accreditation Commission of ABET, www.abet.org.
Program Delivery
This program is available:
via classroom (the majority of instruction is face-to-face)
Admission Requirements
Students must complete
9 courses
before admission to the program.
Freshman and transfer students are usually admitted to pre-major status
before admission to this major.
Students interested in pursuing a degree in computer engineering or electrical engineering are encouraged to take EE 1001 in their first year.
PHYS 1301W - Introductory Physics for Science and Engineering I
[PHYS, WI]
(4.0 cr)
or
PHYS 1401V - Honors Physics I
[PHYS, WI]
(4.0 cr)
PHYS 1302W - Introductory Physics for Science and Engineering II
[PHYS, WI]
(4.0 cr)
or
PHYS 1402V - Honors Physics II
[PHYS, WI]
(4.0 cr)
Required prerequisites
Lower Division Core Courses Required for Admission to Upper Division
EE 2015 - Signals, Circuits and Electronics
(4.0 cr)
EE 2301 - Introduction to Digital System Design
(4.0 cr)
EE 1301 - Introduction to Computing Systems
(4.0 cr)
or
CSCI 1113 - Introduction to C/C++ Programming for Scientists and Engineers
(4.0 cr)
or
CSCI 1133 - Introduction to Computing and Programming Concepts
(4.0 cr)
CSCI 1913 - Introduction to Algorithms, Data Structures, and Program Development
(4.0 cr)
or
CSCI 1933 - Introduction to Algorithms and Data Structures
(4.0 cr)
General Requirements
All students in baccalaureate degree programs are required to complete general University and college requirements including writing and liberal education courses. For more information about University-wide requirements, see the
liberal education requirements.
Required courses for the major, minor or certificate in which a student receives a D grade (with or without plus or minus) do not count toward the major, minor or certificate (including transfer courses).
Program Requirements
All freshmen in the College of Science and Engineering must complete CSE 1001: First-Year Experience. At least 26 upper division credits in the major must be taken at the University of Minnesota Twin Cities Campus.
Lower Division Required Courses
Mathematics
MATH 2374 - CSE Multivariable Calculus and Vector Analysis
(4.0 cr)
Students are required to take one upper division writing intensive course within the major; students must take the following course which also fulfills the Senior Design Project requirement.
Computer Engineering Technical Electives and Specializations
Computer Engineering Technical Electives
Students must complete a total of 28 technical elective credits, with a minimum of 22 credits coming from the Core Department Electives (EE 4xxx/5xxx or CSCI 4xxx/5xxx courses).
Take 28 or more credit(s) from the following:
Core Department Electives
The 22 credits of core department technical electives include EE 4xxx/5xxx and/or CSCI 4xxx/5xxx courses. The following three courses are excluded from the core 22 credits: CSCI 4921, EE 4981H, and EE 4982V.
Take 2 EE or CSCI lab courses from the following list. Students who complete the honors project (EE 4981H and EE 4982V) only need to complete 1 lab course.
Take 2 or more course(s) from the following:
·
EE 4111 - Advanced Analog Electronics Design
(4.0 cr)
·
EE 4163 - Energy Conversion and Storage Laboratory
(1.0 cr)
·
EE 4235 - Linear Control Systems Laboratory
(1.0 cr)
·
EE 4237 - State Space Control Laboratory
(1.0 cr)
·
EE 4301 - Digital Design With Programmable Logic
(4.0 cr)
·
CSCI 5611 - Animation & Planning in Games
(3.0 cr)
·
CSCI 5619 - Virtual Reality and 3D Interaction
(3.0 cr)
·
Additional Approved Technical Electives
Students may complete up to 6 credits of additional electives outside the core department electives toward the technical elective requirement. The list is not exhaustive, and students are encouraged to consult with their departmental advisor for additional options.
The Co-op Program provides students with a professional work experience that takes place over two semesters. Students must complete both CSE4896 and CSE4996 in order to receive EE technical elective credit.
Take 0 - 4 credit(s) from the following:
·
CSE 4896 - Cooperative Industrial Assignment I
(2.0 cr)
·
CSE 4996 - Cooperative Industrial Assignment II
(2.0 cr)
·
Other Business, Law, and Entrepreneurial Related Courses
·
MOT 4001 - Leadership, Professionalism and Business Basics for Engineers
(2.0 cr)
·
Honors Project
The Honors Project provides students with a research experience that takes place over two semesters. Students must complete both semesters to receive credit toward their technical elective program, and this will automatically be reflected upon registration for the second course.
·
EE 4982V - Senior Honors Project II
[WI]
(2.0 cr)
Program Sub-plans
A sub-plan is not required for this program.
Integrated Bachelor of Computer Eng/Master of Science in Electrical and Computer Eng.
The Department of Electrical and Computer Engineering offers an integrated Bachelor’s and Master’s Degree program. Students accepted to the integrated program will be guaranteed admission to the Electrical and Computer Engineering MS as long as they complete their undergraduate program. Accepted students will not need to take the GRE exam or submit Letters of Recommendation as part of their graduate application, unlike other students applying to our graduate programs.
Applicants must be enrolled University of Minnesota Twin Cities students admitted to an Electrical Engineering or Computer Engineering undergraduate program. Applicants must meet a Technical GPA minimum of 3.4 (as defined by the College of Science & Engineering) or have at least a 3.2 GPA and additional positive factors that make them eligible.
Students are eligible to apply after they have completed EE 3015, EE 3101, EE 3115, and a minimum of three additional credits of EE 3xxx level, EE 4xxx level or CSCI 4xxx level coursework. Depending on application materials and timing, an applicant may be asked to wait for another semester of grades before being admitted or rejected.
Full application instructions can be found at https://cse.umn.edu/ece/integrated-degree-program.
Courses that will be used to fulfill Master’s degree requirements must appear in this sub-plan by the tenth day of the semester in which the student is enrolled in the courses.
Any final edits or updates to this sub-pan must be reflected on the APAS no later than the last day of instruction in the semester in which the undergraduate degree will be awarded. Courses not in this sub-plan by that time cannot be updated at a later time; and, therefore will not be eligible for use towards the Master’s degree.
Students can transfer a maximum of 16 credits to the graduate program taken during their integrated senior undergraduate year. Credits being applied to the Master's in Electrical and Computer Engineering taken while the student is an undergraduate for use in the integrated program can also be applied later to an Electrical Engineering Ph.D. within our department if a student applies and is admitted. Credits cannot also be applied to the undergraduate degree (i.e., no “double dipping”).
Integrated Bachelor of Computer Engineering/Master of Science in Computer Science and Eng
The Department of Computer Science & Engineering offers an integrated Bachelor’s and Master’s Degree program. Students accepted to the integrated program will be guaranteed admission to the Computer Science MS as long as they complete their undergraduate program. Accepted students will not need to take the GRE exam as part of their graduate application, unlike other students applying to our graduate programs.
Applicants must be enrolled University of Minnesota Twin Cities students admitted to a Computer Science or Computer Engineering undergraduate program. Applicants must meet a Technical GPA minimum of 3.5 (as defined by the College of Science & Engineering) or they must have a strong recommendation from a Computer Science and Engineering faculty member or instructor (not an ECE Faculty member).
Applicants must have at least 75 credits completed at the time of their application.
Applicants must have passed with a C- or better all of the following courses:
CSCI 1933 or 1913
CSCI 2011
CSCI 2021 (CSCI students) or EE 2361 (CompE students)
CSCI 2033 or a math course containing linear algebra content
CSCI 2041 (CSCI students only)
CSCI 3081W (CSCI students only), CSCI 4041, and CSCI 4061 (applicants can have one of these courses in progress at the time of application)
Full application instructions can be found at cs.umn.edu/integrated
Courses that will be used to fulfill Master’s degree requirements must appear in this sub-plan by the tenth day of the semester in which the student is enrolled in the courses.
Any final edits or updates to this sub-pan must be reflected on the APAS no later than the last day of instruction in the semester in which the undergraduate degree will be awarded. Courses not in this sub-plan by that time cannot be updated at a later time; and, therefore will not be eligible for use towards the Master’s degree.
Students can transfer a maximum of 16 credits to the graduate program taken during their integrated senior undergraduate year. Students must spend a minimum of two semesters as a graduate student after the completion of their undergraduate degree. Coursework applied to the graduate degree must be taken at the graduate level (i.e., 5xxx or above) Credits being applied to the Computer Science Master’s taken while the student is an undergraduate for use in the integrated program can also be applied later to a Computer Science Ph.D. within our department if a student applies and is admitted. Credits cannot also be applied to the undergraduate degree (i.e., no “double dipping”).
Students should consider taking the following courses/requirements to apply to their graduate degree as an undergraduate integrated program student (16 credits max):
CSCI 8970 - Computer Science Colloquium (1 credit)
Course to meet the Theory and Algorithms Breadth requirement (3 credits)*
Course to meet the Architecture, Systems, & Software Breadth requirement (3 credits)*
Course to meet the Applications Breadth requirement (3 credits)*
CSCI 5XXX level course that fits your interests and background (3 credits) or an approved graduate level elective or graduate minor course. We recommend waiting to take CSCI 8XXX level courses for your graduate year, but this level of coursework is still available to you if you have the appropriate prerequisites.
CSCI 5XXX level course that fits your interests and background (3 credits) or an approved graduate level elective or graduate minor course. We recommend waiting to take CSCI 8XXX level courses for your graduate year, but this level of coursework is still available to you if you have the appropriate prerequisites.
*Please refer to the Department of Computer Science & Engineering webpage for more details on which courses count for specific breadth requirements.
One Stop for tuition, course registration, financial aid, academic calendars, and more
MATH 1271 - Calculus I
(MATH)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 1271/Math 1381/Math 1571/
Typically offered:
Every Fall, Spring & Summer
Differential calculus of functions of a single variable, including polynomial, rational, exponential, and trig functions. Applications, including optimization and related rates problems. Single variable integral calculus, using anti-derivatives and simple substitution. Applications may include area, volume, work problems.
prereq: 4 yrs high school math including trig or satisfactory score on placement test or grade of at least C- in [1151 or 1155]
MATH 1371 - CSE Calculus I
(MATH)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 1271/Math 1381/Math 1571/
Typically offered:
Every Fall & Spring
Differentiation of single-variable functions, basics of integration of single-variable functions. Applications: max-min, related rates, area, curve-sketching. Use of calculator, cooperative learning.
prereq: CSE or pre-bioprod concurrent registration is required (or allowed) in biosys engn (PRE), background in [precalculus, geometry, visualization of functions/graphs], instr consent; familiarity with graphing calculators recommended
MATH 1571H - Honors Calculus I
(MATH)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 1271/Math 1381/Math 1571/
Grading Basis:
A-F only
Typically offered:
Every Fall
Differential/integral calculus of functions of a single variable. Emphasizes hard problem-solving rather than theory.
prereq: Honors student and permission of University Honors Program
MATH 1272 - Calculus II
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 1272/Math 1282/Math 1372/
Typically offered:
Every Fall, Spring & Summer
Techniques of integration. Calculus involving transcendental functions, polar coordinates. Taylor polynomials, vectors/curves in space, cylindrical/spherical coordinates.
prereq: [1271 or equiv] with grade of at least C-
MATH 1372 - CSE Calculus II
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 1272/Math 1282/Math 1372/
Typically offered:
Every Spring
Techniques of integration. Calculus involving transcendental functions, polar coordinates, Taylor polynomials, vectors/curves in space, cylindrical/spherical coordinates. Use of calculators, cooperative learning.
prereq: Grade of at least C- in [1371 or equiv], CSE or pre-Bioprod/Biosys Engr
MATH 1572H - Honors Calculus II
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 1272/Math 1282/Math 1372/
Grading Basis:
A-F only
Typically offered:
Every Fall & Spring
Continuation of 1571. Infinite series, differential calculus of several variables, introduction to linear algebra.
prereq: 1571H (or equivalent) honors student
MATH 2243 - Linear Algebra and Differential Equations
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 2243/Math 2373/Math 2574H
Typically offered:
Every Fall, Spring & Summer
Linear algebra: basis, dimension, matrices, eigenvalues/eigenvectors. Differential equations: first-order linear, separable; second-order linear with constant coefficients; linear systems with constant coefficients.
prereq: [1272 or 1282 or 1372 or 1572] w/grade of at least C-
MATH 2373 - CSE Linear Algebra and Differential Equations
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 2243/Math 2373/Math 2574H
Typically offered:
Every Fall & Spring
Linear algebra: basis, dimension, eigenvalues/eigenvectors. Differential equations: linear equations/systems, phase space, forcing/resonance, qualitative/numerical analysis of nonlinear systems, Laplace transforms. Use of computer technology.
prereq: [1272 or 1282 or 1372 or 1572] w/grade of at least C-, CSE or pre-Bio Prod/Biosys Engr
MATH 2574H - Honors Calculus IV
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 2243/Math 2373/Math 2574H
Grading Basis:
A-F only
Typically offered:
Every Spring
Advanced linear algebra, differential equations. Additional topics as time permits.
prereq: Math 1572H or Math 2573H, honors student and permission of University Honors Program
PHYS 1301W - Introductory Physics for Science and Engineering I
(PHYS, WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Phys 1201W/1301W/1401V/1501V
Typically offered:
Every Fall, Spring & Summer
Use of fundamental principles to solve quantitative problems. Motion, forces, conservation principles, structure of matter. Applications to mechanical systems.
Prereq or Concurrent: MATH 1271/1371/1371H or equivalent
PHYS 1401V - Honors Physics I
(PHYS, WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Phys 1201W/1301W/1401V/1501V
Grading Basis:
A-F only
Typically offered:
Every Fall
Comprehensive, calculus-level general physics. Emphasizes use of fundamental principles to solve quantitative problems. Description of motion, forces, conservation principles. Structure of matter, with applications to mechanical systems.
Prereq: Honors program or with permission,
Prereq or Concurrent: MATH 1271/1371/1571H or equivalent
PHYS 1302W - Introductory Physics for Science and Engineering II
(PHYS, WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Phys 1202W/1302W/1402V/1502V
Typically offered:
Every Fall & Spring
Use of fundamental principles to solve quantitative problems. Motion, forces, conservation principles, fields, structure of matter. Applications to electromagnetic phenomena.
Prereq: PHYS 1301 or equivalent,
Prereq or Concurrent: MATH 1272/1372/1572H or equivalent
PHYS 1402V - Honors Physics II
(PHYS, WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Phys 1202W/1302W/1402V/1502V
Grading Basis:
A-F only
Typically offered:
Every Spring
Fundamental principles to solve quantitative problems. Description of motion, forces, conservation principles, fields. Structure of matter, with applications to electro-magnetic phenomena.
Honors program or with permission, PHYS 1401V or equivalent, Prereq or CC: MATH 1272/1372/1572H or equivalent
EE 2015 - Signals, Circuits and Electronics
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall, Spring & Summer
Introduction to analog electrical systems with particular emphasis on audio circuits and signals. Time and frequency domain representations. Kirchhoff?s laws. Power. Inductance and Capacitance. Introduction to op-amp circuits and their audio applications. Complex numbers and phasors. Introduction to Fourier Series. RLC circuits and basic filter networks. Laboratory experiments on audio amplifiers, distortion, intermodulation products, low-level differential amplifiers, bass/treble filters.
prereq: concurrent registration is required (or allowed) in PHYS 1302, concurrent registration is required (or allowed) in (MATH 2243 or MATH 2373 or MATH 2573)
EE 2301 - Introduction to Digital System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Boolean algebra, logic gates, combinational logic, logic simplification, sequential logic, design of synchronous sequential logic, Verilog modeling, design of logic circuits. Integral lab.
Prereq: [EE 1301 (preferred) or CSCI 1113 or CSCI 1103 or CSci 1133]
EE 1301 - Introduction to Computing Systems
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
C/C++ programming constructs, binary arithmetic and bit manipulation, data representation and abstraction, data types/structures, arrays, pointer addressing, control flow, iteration, recursion, file I/O, basics of object-oriented programming. An Internet-of-Things lab is integral to the course.
CSCI 1113 - Introduction to C/C++ Programming for Scientists and Engineers
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall, Spring & Summer
Programming for scientists/engineers. C/C++ programming constructs, object-oriented programming, software development, fundamental numerical techniques. Exercises/examples from various scientific fields.
The online modality for CSci 1113 will only be offered during the summer session.
prereq: Math 1271 or Math 1371 or Math 1571H or instr consent.
CSCI 1133 - Introduction to Computing and Programming Concepts
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 1133/CSci 1133H
Typically offered:
Every Fall, Spring & Summer
Fundamental programming concepts using Python language. Problem solving skills, recursion, object-oriented programming. Algorithm development techniques. Use of abstractions/modularity. Data structures/abstract data types. Develop programs to solve real-world problems.
prereq: concurrent registration is required (or allowed) in MATH 1271 or concurrent registration is required (or allowed) in MATH 1371 or concurrent registration is required (or allowed) in MATH 1571H or instr consent
CSCI 1913 - Introduction to Algorithms, Data Structures, and Program Development
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall, Spring & Summer
Advanced object oriented programming to implement abstract data types(stacks, queues, linked lists, hash tables, binary trees) using Java language. Searching/sorting algorithms. Basic algorithmic analysis. Scripting languages using Python language. Substantial programming projects. Weekly lab.
prereq: (EE major and EE 1301) or (CmpE major and EE 1301) or 1103 or 1113 or instr consent
CSCI 1933 - Introduction to Algorithms and Data Structures
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 1902/CSci 1933/CSci 1933H
Typically offered:
Every Fall, Spring & Summer
Advanced object oriented programming to implement abstract data types (stacks, queues, linked lists, hash tables, binary trees) using Java language. Inheritance. Searching/sorting algorithms. Basic algorithmic analysis. Use of software development tools. Weekly lab.
prereq: 1133 or instr consent
MATH 2374 - CSE Multivariable Calculus and Vector Analysis
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 2263/Math 2374/Math 2573H
Typically offered:
Every Fall & Spring
Derivative as linear map. Differential/integral calculus of functions of several variables, including change of coordinates using Jacobians. Line/surface integrals. Gauss, Green, Stokes theorems. Use of computer technology.
prereq: [1272 or 1282 or 1372 or 1572] w/grade of at least C-, CSE or pre-Bioprod/Biosys Engr
MATH 2263 - Multivariable Calculus
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 2263/Math 2374/Math 2573H
Typically offered:
Every Fall, Spring & Summer
Derivative as linear map. Differential/integral calculus of functions of several variables, including change of coordinates using Jacobians. Line/surface integrals. Gauss, Green, Stokes Theorems.
prereq: [1272 or 1372 or 1572] w/grade of at least C-
MATH 2573H - Honors Calculus III
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 2263/Math 2374/Math 2573H
Grading Basis:
A-F only
Typically offered:
Every Fall
Integral calculus of several variables. Vector analysis, including theorems of Gauss, Green, Stokes.
prereq: Math 1572H (or equivalent), honors student
CSCI 2011 - Discrete Structures of Computer Science
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 2011/CSci 2011H
Typically offered:
Every Fall & Spring
Foundations of discrete mathematics. Sets, sequences, functions, big-O, propositional/predicate logic, proof methods, counting methods, recursion/recurrences, relations, trees/graph fundamentals.
prereq: MATH 1271 or MATH 1371 or instr consent
EE 2115 - Analog and Digital Electronics
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall, Spring & Summer
An introduction to electronic circuits with emphasis on switching speed and analog mixed signal models. Transient analysis of RC, RL and RLC circuits. Gate delays and limitations on CMOS digital circuit switching. Transient response of lumped 1st and 2nd order ladder networks. Laplace transform and applications. Introduction to analog filters. Elementary sampled data filters. A/D and D/A circuit technologies. Laboratory experiments on AM modulation and superheterodyne receivers with focus on electronic implementation.
prereq: 2015
EE 2361 - Introduction to Microcontrollers
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall, Spring & Summer
Basic computer organization, opcodes, assembly language programming, logical operations and bit manipulation in C, stack structure, timers, parallel/serial input/output, buffers, input pulse-width and period measurements, PWM output, interrupts and multi-tasking, using special-purpose features such as A/D converters. Integral lab.
Prereq: [EE 2301]
CSCI 4041 - Algorithms and Data Structures
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4041/CSci 4041H
Typically offered:
Every Fall & Spring
Rigorous analysis of algorithms/implementation. Algorithm analysis, sorting algorithms, binary trees, heaps, priority queues, heapsort, balanced binary search trees, AVL trees, hash tables and hashing, graphs, graph traversal, single source shortest path, minimum cost spanning trees.
prereq: [(1913 or 1933) and 2011] or instr consent; cannot be taken for grad CSci cr
CSCI 4061 - Introduction to Operating Systems
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4061/INet 4001
Typically offered:
Every Fall & Spring
Processes/threads, process coordination, interprocess communication, asynchronous events, memory management/file systems. Systems programming projects using operating system interfaces and program development tools.
prereq: 2021 or EE 2361; CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci.
EE 3015 - Signals and Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall & Spring
Basic techniques for analysis/design of signal processing, communications, and control systems. Time/frequency models, Fourier-domain representations, modulation. Discrete-time/digital signal/system analysis. Z transform. State models, stability, feedback. Suggest taking EE 3101 concurrently.
prereq: [2115, CSE Upper Division] or dept consent
EE 3025 - Statistical Methods in Electrical and Computer Engineering
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall, Spring & Summer
Notions of probability. Elementary statistical data analysis. Random variables, densities, expectation, correlation. Random processes, linear system response to random waveforms. Spectral analysis. Computer experiments for analysis and design in random environment.
prereq: [3015, CSE upper division] or instr approval
EE 3101 - Signals, Circuits and Electronics Laboratory
Credits:
1.0
[max 2.0]
Grading Basis:
A-F only
Typically offered:
Every Fall & Spring
Experiments in electronic systems for information processing; modulation, demodulation, and filtering using analog and digital electronics; sampling, quantization and digital filtering; feedback and phase lock loops.
prereq: [2115, &3015, &3115, CSE Upper Division] or dept consent
EE 3115 - Analog Electronics
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall, Spring & Summer
Basic differential amplifiers using FETs and BJTs. Current sources for differential amplifiers. Op- amp-based differential amplifiers. IC op amps as multi-stage amplifiers. Ideal (dc) feedback. Stability and compensation of negative feedback amplifiers. Sinusoidal oscillators. Waveshaping circuits. Power amplifiers. Use of circuit simulators. EE 3015 and EE 3101 should be taken before or concurrently with EE 3115.
prereq: [EE 2115, &EE 3015, CSE upper division] or dept consent
EE 3951W - Junior Design Project
(WI)
Credits:
2.0
[max 2.0]
Course Equivalencies:
EE 3102/EE 3951W
Typically offered:
Every Fall & Spring
Prereq - EE 3101.Team participation in formulating/solving a structured common design problem emphasizing instrumentation systems. Oral/written presentations.
EE 4363 - Computer Architecture and Machine Organization
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4203/EE 4363
Typically offered:
Every Fall & Spring
Introduction to computer architecture. Aspects of computer systems, such as pipelining, memory hierarchy, and input/output systems. Performance metrics. Examines each component of a complicated computer system.
prereq: 2361
CSCI 4203 - Computer Architecture
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4203/EE 4363
Typically offered:
Every Fall & Spring
Introduction to computer architecture. Aspects of computer systems, such as pipelining, memory hierarchy, and input/output systems. Performance metrics. Examins each component of a complicated computer system.
prereq: 2021 or instr consent
EE 4951W - Senior Design Project
(WI)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Team participation in formulating/solving open-ended design problems. Oral/written presentations.
prereq: 3015, 3115, 3102, attendance first day of class
EE 4951W - Senior Design Project
(WI)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Team participation in formulating/solving open-ended design problems. Oral/written presentations.
prereq: 3015, 3115, 3102, attendance first day of class
EE 4111 - Advanced Analog Electronics Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Basic integrated circuit building blocks of differential amplifiers, high bandwidth, instrumentation amplifiers. Current/voltage references. Feedback, stability, and noise in electronic circuits. Integral lab.
prereq: 3015, 3115
EE 4163 - Energy Conversion and Storage Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Provides laboratory experiences with the topics of 4161W, including the fundamental physics and chemistry of selected energy conversion and energy storage devices, their application, and their connection strategies in electric power applications.
prereq: concurrent registration is required (or allowed) in 4161W
EE 4235 - Linear Control Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Lab to accompany 4231.
prereq: 4231 or concurrent registration is required (or allowed) in 4231
EE 4237 - State Space Control Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Lab to accompany 4233.
prereq: 4233 or concurrent registration is required (or allowed) in 4233; no cr for [EE or CompE] grad students
EE 4301 - Digital Design With Programmable Logic
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Summer
Introduction to system design/simulation. Design using Verilog code/synthesis. Emulation using Verilog code.
prereq: 2301, [1301 or CSCI 1113 or CSCI 1901]
EE 4341 - Embedded System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microcontroller interfacing for embedded system design. Exception handling/interrupts. Memory Interfacing. Parallel/serial input/output methods. System Buses and protocols. Serial Buses and component interfaces. Microcontroller Networks. Real-Time Operating Systems. Integral lab.
prereq: 2301, 2361, upper div CSE
EE 4505 - Communications Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Experiments in analysis/design of wired/wireless communication systems. Lab to accompany 4501.
prereq: 4501 or concurrent registration is required (or allowed) in 4501
EE 4703 - Electric Drives Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Laboratory to accompany 4701. Simulink-based simulations of electric machines/drives in applications such as energy conservation and motion control in robotics.
prereq: 4701 or concurrent registration is required (or allowed) in 4701
EE 4722 - Power System Analysis Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Lab analysis of AC power systems, power system networks, power flow, short circuit, transient stability.
prereq: 4721 or concurrent registration is required (or allowed) in 4721
EE 4743 - Switch-Mode Power Electronics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Laboratory to accompany 4741. PSpice-/Simulink-based simulations of converters, topologies, and control in switch-mode dc power supplies, motor drives for motion control, and inverters for interfacing renewable energy sources to utility grid.
prereq: 4741 or concurrent registration is required (or allowed) in 4741
EE 4930 - Special Topics in Electrical and Computer Engineering Laboratory
Credits:
1.0
-2.0
[max 6.0]
Grading Basis:
A-F only
Typically offered:
Periodic Fall, Spring & Summer
Lab work not available in regular courses. Topics vary.
prereq: CSE sr or grad student or instr consent
EE 5141 - Introduction to Microsystem Technology
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microelectromechanical systems composed of microsensors, microactuators, and electronics integrated onto common substrate. Design, fabrication, and operation principles. Labs on micromachining, photolithography, etching, thin film deposition, metallization, packaging, and device characterization.
prereq: [3161, 3601, CSE grad student] or dept consent
EE 5173 - Basic Microelectronics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Students fabricate a polysilicon gate, single-layer metal, NMOS chip, performing 80 percent of processing, including photolithography, diffusion, oxidation, and etching. In-process measurement results are compared with final electrical test results. Simple circuits are used to estimate technology performance.
prereq: [[5171 or concurrent registration is required (or allowed) in 5171], CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation.
prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5373 - Data Modeling Using R
Credits:
1.0
[max 1.0]
Grading Basis:
A-F only
Typically offered:
Periodic Fall & Spring
Introduction to data modeling and the R language programming. Multi-factor linear regression modeling.
Residual analysis and model quality evaluation. Response prediction. Training and testing. Integral lab.
An introductory course in probability and statistics is suggested but not required; basic programming skills in some high-level programming language, such as C/C++, Java, Fortran, etc also suggested.
EE 5545 - Digital Signal Processing Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Real-time implementation of digital signal processing (DSP) algorithms, including filtering, sample-rate conversion, and FFT-based spectral analysis. Implementation on a modern DSP Platform. Processor architecture. Arithmetic operations. Real-time processing issues. Processor limitations. Integral laboratory.
prereq: [4541, CSE grad student] or dept consent
EE 5613 - RF/Microwave Circuit Design Laboratory
Credits:
2.0
[max 2.0]
Grading Basis:
A-F only
Typically offered:
Every Spring
Scattering parameters, planar lumped circuits, transmission lines, RF/microwave substrate materials, matching networks/tuning elements, resonators, filters, combiners/dividers, couplers. Integral lab.
prereq: [[5601 or concurrent registration is required (or allowed) in 5601], CSE grad student] or dept consent
EE 5622 - Physical Optics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Fundamental optical techniques. Diffraction and optical pattern recognition. Spatial/temporal coherence. Interferometry. Speckle. Coherent/incoherent imaging. Coherent image processing. Fiber Optics.
prereq: [[5621 or concurrent registration is required (or allowed) in 5621], CSE grad student] or dept consent
EE 5657 - Physical Principles of Thin Film Technology
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Fabrication, characterization, and application of thin film and nanostructured materials and devices. Focuses on vacuum deposition. Materials science. Hands-on, team-based labs.
EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Periodic Spring
Lab to accompany 5705.
prereq: 5705 or concurrent registration is required (or allowed) in 5705
EE 5811 - Biological Instrumentation
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
This course will cover the physics and technology of biological instruments. The operating principles of optical, electrical, and mechanical biosensors will be discussed, followed by transport and delivery of biomolecules to the sensors. Techniques to manufacture these sensing devices, along with microfluidic packaging, will be covered. Lectures will be complemented by lab demo sessions to give students hands-on experiences in microfluidic chip fabrication, microscopy, and particle trapping experiments.
CSCI 4511W - Introduction to Artificial Intelligence
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Typically offered:
Every Fall & Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit.
prereq: 2041 or instr consent
CSCI 5511 - Artificial Intelligence I
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Prerequisites:
[2041 or #], grad student
Typically offered:
Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language.
prereq: [2041 or instr consent], grad student
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics.
prereq: 2031 or 2033 or instr consent
EE 4389W - Introduction to Predictive Learning
(WI)
Credits:
3.0
[max 3.0]
Typically offered:
Fall Odd Year
Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies.
prereq: [3025, ECE student] or STAT 3022; computer programming or MATLAB or similar environment is recommended for ECE students
EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.
A previous course in computer architecture is suggested but not required.
EE 5351 - Applied Parallel Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Parallel programming/architecture. Application development for many-core processors. Computational thinking, types of parallelism, programming models, mapping computations effectively to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, application case studies.
prereq: [4363 or equivalent], programming experience (C/C++ preferred)
EE 5364 - Advanced Computer Architecture
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5204/EE 5364
Typically offered:
Every Fall
Instruction set architecture, processor microarchitecture. Memory and I/O systems. Interactions between computer software and hardware. Methodologies of computer design.
prereq: [[4363 or CSci 4203], CSE grad student] or dept consent
EE 5371 - Computer Systems Performance Measurement and Evaluation
Credits:
3.0
[max 3.0]
Course Equivalencies:
EE 5371/5863
Typically offered:
Periodic Fall & Spring
Tools/techniques for analyzing computer hardware, software, system performance. Benchmark programs, measurement tools, performance metrics. Deterministic/probabilistic simulation techniques, random number generation/testing. Bottleneck analysis.
prereq: [4363 or 5361 or CSci 4203 or 5201], [CSE grad student] or dept consent
EE 5393 - Circuits, Computation, and Biology
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Connections between digital circuit design and synthetic/computational biology. Probabilistic, discrete-event simulation. Timing analysis. Information-Theoretic Analysis. Feedback in digital circuits/genetic regulatory systems. Synthesizing stochastic logic and probabilistic biochemistry.
CSCI 5204 - Advanced Computer Architecture
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5204/EE 5364
Typically offered:
Every Fall
Instruction set architecture, processor microarchitecture, memory, I/O systems. Interactions between computer software and hardware. Methodologies of computer design.
prereq: 4203 or EE 4363
EE 4231 - Linear Control Systems: Designed by Input/Output Methods
Credits:
3.0
[max 3.0]
Course Equivalencies:
AEM 4321/EE 4231
Typically offered:
Every Fall
Modeling, characteristics, performance of feedback control systems. Stability, root locus, frequency response methods. Digital implementation, hardware considerations.
prereq: [3015, [upper div CSE or grad student in CSE major]] or instr consent
EE 4233 - State Space Control System Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
State space models, performance evaluation, numerical issues for feedback control. Stability, state estimation, quadratic performance. Implementation, computational issues.
prereq: [3015, upper div CSE] or instr consent
EE 4341 - Embedded System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microcontroller interfacing for embedded system design. Exception handling/interrupts. Memory Interfacing. Parallel/serial input/output methods. System Buses and protocols. Serial Buses and component interfaces. Microcontroller Networks. Real-Time Operating Systems. Integral lab.
prereq: 2301, 2361, upper div CSE
EE 5271 - Robot Vision
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Modern visual perception for robotics that includes position and orientation, camera model and calibration, feature detection, multiple images, pose estimation, vision-based control, convolutional neural networks, reinforcement learning, deep Q-network, and visuomotor policy learning. [Math 2373 or equivalent; EE 1301 or equivalent basic programming course]
EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.
A previous course in computer architecture is suggested but not required.
CSCI 4511W - Introduction to Artificial Intelligence
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Typically offered:
Every Fall & Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit.
prereq: 2041 or instr consent
CSCI 5143 - Real-Time and Embedded Systems
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Periodic Spring
Real-time systems that require timely response by computer to external stimulus. Embedded systems in which computer is part of machine. Increasing importance of these systems in commercial products. How to control robots and video game consoles. Lecture, informal lab.
prereq: [4061 or instr consent], experience with C language
CSCI 5511 - Artificial Intelligence I
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Prerequisites:
[2041 or #], grad student
Typically offered:
Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language.
prereq: [2041 or instr consent], grad student
CSCI 5512 - Artificial Intelligence II
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5512W/CSci 5512
Typically offered:
Every Spring
Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods.
prereq: [STAT 3021, 4041] or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence.
Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5525 - Machine Learning: Analysis and Methods
Credits:
3.0
[max 3.0]
Typically offered:
Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models.
prereq: Grad student or instr consent
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics.
prereq: 2031 or 2033 or instr consent
CSCI 5552 - Sensing and Estimation in Robotics
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping.
prereq: [5551, Stat 3021] or instr consent
CSCI 5561 - Computer Vision
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications.
prereq: CSci 5511, 5521, or instructor consent.
EE 4301 - Digital Design With Programmable Logic
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Summer
Introduction to system design/simulation. Design using Verilog code/synthesis. Emulation using Verilog code.
prereq: 2301, [1301 or CSCI 1113 or CSCI 1901]
EE 5301 - VLSI Design Automation I
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Basic graph/numerical algorithms. Algorithms for logic/high-level synthesis. Simulation algorithms at logic/circuit level. Physical-design algorithms.
prereq: [2301, CSE grad student] or dept consent
EE 5302 - VLSI Design Automation II
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Basic algorithms, computational complexity. High-level synthesis. Test generation. Power estimation. Timing optimization. Current topics.
prereq: [5301, CSE grad student] or dept consent
EE 5323 - VLSI Design I
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Combinational static CMOS circuits. Transmission gate networks. Clocking strategies, sequential circuits. CMOS process flows, design rules, structured layout techniques. Dynamic circuits, including Domino CMOS and DCVS. Performance analysis, design optimization, device sizing.
prereq: [2301, 3115, CSE grad student] or dept consent
EE 5324 - VLSI Design II
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
CMOS arithmetic logic units, high-speed carry chains, fast CMOS multipliers. High-speed performance parallel shifters. CMOS memory cells, array structures, read/write circuits. Design for testability, including scan design and built-in self test. VLSI case studies.
prereq: [5323, CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation.
prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5329 - VLSI Digital Signal Processing Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Programmable architectures for signal/media processing. Data-flow representation. Architecture transformations. Low-power design. Architectures for two's complement/redundant representation, carry-save, and canonic signed digit. Scheduling/allocation for high-level synthesis.
prereq: [[5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5333 - Analog Integrated Circuit Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Fundamental circuits for analog signal processing. Design issues associated with MOS/BJT devices. Design/testing of circuits. Selected topics (e.g., modeling of basic IC components, design of operational amplifier or comparator or analog sampled-data circuit filter).
prereq: [3115, CSE grad student] or dept consent
EE 4501 - Communications Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Systems for transmission/reception of digital/analog information. Characteristics/design of wired/wireless communication systems. Baseband, digital, and carrier-based techniques. Modulation. Coding. Electronic noise and its effects on design/performance.
prereq: 3025
CSCI 4131 - Internet Programming
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4131/CSci 5131
Typically offered:
Every Fall & Spring
Issues in internet programming. Internet history, architecture/protocols, network programming, Web architecture. Client-server architectures and protocols. Client-side programming, server-side programming, dynamic HTML, Java programming, object-oriented architecture/design, distributed object computing, Web applications.
prereq: 4061, 4211 recommended, cannot be taken for grad CSci cr
CSCI 4211 - Introduction to Computer Networks
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4211/CSci 5211/INET 4002
Typically offered:
Every Fall & Spring
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, routing, transport, network programming interfaces, networked applications. Examples from Ethernet, Token Ring, TCP/IP, HTTP, WWW.
prereq: 4061 or instr consent; basic knowledge of [computer architecture, operating systems] recommended, cannot be taken for grad CSci cr
CSCI 5211 - Data Communications and Computer Networks
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4211/CSci 5211/INET 4002
Typically offered:
Every Fall
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, network layer/routing protocols, transport, congestion/flow control, emerging high-speed networks, network programming interfaces, networked applications. Case studies using Ethernet, Token Ring, FDDI, TCP/IP, ATM, Email, HTTP, and WWW.
prereq: [4061 or instr consent], basic knowledge of [computer architecture, operating systems, probability], grad student
CSCI 5221 - Foundations of Advanced Networking
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Design principles, protocol mechanisms. Network algorithmics, implementation techniques. Advanced network architectures, state-of-art/emerging networking technologies/applications, network modeling. Simulation, experiments.
prereq: 4211 or 5211 or equiv; intro course in computer networks recommended
CSCI 5271 - Introduction to Computer Security
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Concepts of computer, network, and information security.
Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls.
prereq: 4061 or 5103 or equiv or instr consent
CSCI 5471 - Modern Cryptography
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Introduction to cryptography. Theoretical foundations, practical applications. Threats, attacks, and countermeasures, including cryptosystems and cryptographic protocols. Secure systems/networks. History of cryptography, encryption (conventional, public key), digital signatures, hash functions, message authentication codes, identification, authentication, applications.
prereq: [2011, 4041, [familiarity with number theory or finite fields]] or instr consent
EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
Algorithm techniques for enhancing the scalability of parallel software: scatter-to-gather, problem decomposition, binning, privatization, tiling, regularization, compaction, double-buffering, and data layout. These techniques address the most challenging problems in building scalable parallel software: limited parallelism, data contention, insufficient memory bandwidth, load balance, and communication latency. Programming assignments will be given to reinforce the understanding of the techniques.
prereq: basic knowledge of CUDA, experience working in a Unix environment, and experience developing and running scientific codes written in C or C++. Completion of EE 5351 is not required but highly recommended.
CSCI 4011 - Formal Languages and Automata Theory
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Logical/mathematical foundations of computer science. Formal languages, their correspondence to machine models. Lexical analysis, string matching, parsing. Decidability, undecidability, limits of computability. Computational complexity.
prereq: 2041 or instr consent
CSCI 4707 - Practice of Database Systems
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4707/CSci 5707/INET 4707
Typically offered:
Every Fall & Spring
Concepts, conceptual data models, case studies, common data manipulation languages, logical data models, database design, facilities for database security/integrity, applications.
prereq: 4041 or instr consent
CSCI 5103 - Operating Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Conceptual foundation of operating system designs and implementations. Relationships between operating system structures and machine architectures. UNIX implementation mechanisms as examples.
prereq: 4061 or instr consent
CSCI 5105 - Introduction to Distributed Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Distributed system design and implementation. Distributed communication and synchronization, data replication and consistency, distributed file systems, fault tolerance, and distributed scheduling.
prereq: [5103 or equiv] or instr consent
CSCI 5106 - Programming Languages
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages.
prereq: 4011 or instr consent
CSCI 5117 - Developing the Interactive Web
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Hands-on design experience using modern web development tools. Students work in teams to develop software programs using each of four toolkits. Analyze developments in forum posts and classroom discussions.
prereq: 4131 or 5131 or instr consent; upper div or grad in CSci recommended
CSCI 5125 - Collaborative and Social Computing
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Introduction to computer-supported cooperative work, social computing. Technology, research methods, theory, case studies of group computing systems. Readings, hands-on experience.
prereq: 5115 or instr consent
CSCI 5161 - Introduction to Compilers
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Techniques for translating modern programming languages to intermediate forms or machine-executable instructions/their organization into compiler. Lexical analysis, syntax analysis, semantic analysis, data flow analysis, code generation. Compiler project for prototypical language.
prereq: [2021, 5106] or instr consent
CSCI 5271 - Introduction to Computer Security
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Concepts of computer, network, and information security.
Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls.
prereq: 4061 or 5103 or equiv or instr consent
CSCI 5421 - Advanced Algorithms and Data Structures
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains.
prereq: 4041 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.
prereq: 4041 or instr consent
CSCI 5707 - Principles of Database Systems
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4707/CSci 5707/INET 4707
Typically offered:
Every Fall
Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends.
prereq: [4041 or instr consent], grad student
CSCI 5708 - Architecture and Implementation of Database Management Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules.
prereq: 4041 or 4707 or 5707 or instr. consent
CSCI 5801 - Software Engineering I
Credits:
3.0
[max 3.0]
Prerequisites:
2041 or #
Typically offered:
Every Fall
Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance.
prereq: 2041 or instr consent
CSCI 5802 - Software Engineering II
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Introduction to software testing, software maturity models, cost specification models, bug estimation, software reliability models, software complexity, quality control, and experience report. Student groups specify, design, implement, and test partial software systems. Application of general software development methods and principles from 5801.
prereq: 5801 or instr consent
EE 4521 - Introduction to Machine Learning and Data Science for Electrical and Computer Engineers
Credits:
3.0
[max 3.0]
Course Equivalencies:
EE 4521/EE 5521
Typically offered:
Every Fall
Computational techniques for analysis and inference from data. Python language programming. Elementary numerical optimization and statistical data analysis. Computational methods for clustering, dimensionality reduction, classification, regression, and time series analysis. Construction, training, and utilization of deep neural networks. Application case studies using datasets arising in Electrical and Computer Engineering.
prereq: EE 3025; Math 2263 or 2374; Math 2142, 2243, 2373 or CSci 2033
CSCI 5302 - Analysis of Numerical Algorithms
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Additional topics in numerical analysis. Interpolation, approximation, extrapolation, numerical integration/differentiation, numerical solutions of ordinary differential equations. Introduction to optimization techniques.
prereq: 2031 or 2033 or instr consent
CSCI 5304 - Computational Aspects of Matrix Theory
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Perturbation theory for linear systems and eigenvalue problems. Direct/iterative solution of large linear systems. Matrix factorizations. Computation of eigenvalues/eigenvectors. Singular value decomposition. LAPACK/other software packages. Introduction to sparse matrix methods.
prereq: 2031 or 2033 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.
prereq: 4041 or instr consent
CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Computational methods for analyzing, integrating, and
deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets.
prereq: 3003 or 4041 or instr consent
CSCI 5481 - Computational Techniques for Genomics
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks.
prereq: 4041 or instr consent
CSCI 5523 - Introduction to Data Mining
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects.
prereq: 4041 or equiv or instr consent
CSCI 5609 - Visualization
Credits:
3.0
[max 3.0]
Typically offered:
Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization.
prereq: [1913, 4041] or equiv or instr consent
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc.
prereq: Familiarity with Java, C++, or Python
CSCI 4611 - Programming Interactive Computer Graphics and Games
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Tools/techniques for programming games/interactive computer graphics. Event loops, rendering/animation, polygonal models, texturing, physical simulation. Modern graphics toolkits. History/future of computer games technology. Social impact of interactive computer graphics.
prereq: 2021 or instr consent
CSCI 5115 - User Interface Design, Implementation and Evaluation
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project.
prereq: 4041 or instr consent
In this course, you will learn and apply the principles of embodied computing to human-centered challenges. Through a semester-long team project, you will learn and demonstrate mastery of human-centered embodied computing through two phases: (1) investigating human needs and current embodied practices and (2) rapidly prototyping and iterating embodied computing solutions. One of the ways you will demonstrate this mastery is through the collaborative creation of a written document and project capstone video describing your process and prototype.
prereq: CSci 4041, upper division or graduate student, or instructor permission; CSci 5115 or equivalent recommended.
CSCI 5607 - Fundamentals of Computer Graphics 1
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Fundamental algorithms in computer graphics. Emphasizes programming projects in C/C++. Scan conversion, hidden surface removal, geometrical transformations, projection, illumination/shading, parametric cubic curves, texture mapping, antialising, ray tracing. Developing graphics software, graphics research.
prereq: concurrent registration is required (or allowed) in 2033, concurrent registration is required (or allowed) in 3081
CSCI 5611 - Animation & Planning in Games
Credits:
3.0
[max 3.0]
Typically offered:
Fall Odd Year
Theory behind algorithms used to bring virtual worlds to life. Computer animation topics. Real-time, interactive techniques used in modern games. Physically-based animation, motion planning, character animation, simulation in virtual worlds.
prereq: 4041 or 4611 or instr consent
CSCI 5619 - Virtual Reality and 3D Interaction
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
Introduction to software, technology/applications in virtual/augmented reality, 3D user interaction. Overview of current research. Hands-on projects.
prereq: 4611 or 5607 or 5115 or equiv or instr consent
AEM 2011 - Statics
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall, Spring & Summer
Force/moment vectors, resultants. Principles of statics and free-body diagrams. Applications to simple trusses, frames, and machines. Distributed loads. Internal forces in beams. Properties of areas, second moments. Laws of friction.
prereq: PHYS 1301W, [concurrent registration is required (or allowed) in Math 2374 or equiv], CSE
AEM 2012 - Dynamics
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall, Spring & Summer
Kinematics/kinetics of particles. Newton's laws. Energy/momentum methods. Systems of particles. Kinematics/kinetics of planar motions of rigid bodies. Plane motion of rigid bodies. Mechanical vibrations.
prereq: 2011, [concurrent registration is required (or allowed) in Math 2373 or equiv], CSE student
AEM 2021 - Statics and Dynamics
Credits:
4.0
[max 4.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Force/moment vectors, resultants. Principles of statics and free-body diagrams. Applications to simple trusses, frames, and machines. Properties of areas, second moments. Internal forces in beams. Laws of friction. Principles of particle dynamics. Mechanical systems and rigid-body dynamics. Kinematics/dynamics of plane systems. Energy/momentum of 2-D bodies/systems.
prereq: Phys 1301W, [concurrent registration is required (or allowed) in Math 2374 or equiv], CSE
AEM 3031 - Deformable Body Mechanics
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall, Spring & Summer
Uniaxial loading/deformation. Stress/strain at point, Mohr's circle. Internal forces in beams. Material behavior, linear elasticity. Torsion of circular shafts. Bending of beams of symmetrical section. Column buckling. Statically indeterminate structures.
prereq: [2011 or 2021 or [BMEN 3011, BMEN major]], [Math 2374 or equiv], [concurrent registration is required (or allowed) in Math 2373 or equiv], CSE
AEM 4601 - Instrumentation Laboratory
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Introduction to lab instrumentation. Computerized data acquisition. Statistical analysis of data. Time series data, spectral analysis. Transducers for measurement of solid, fluid, and dynamical quantities. Design of experiments.
prereq: CSci 1113, EE 3005, EE 3006, [upper div BAEM]
AST 2001 - Fundamental Astrophysics
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Physical principles and study of solar system, stars, galaxy, and universe. How observations/conclusions are made.
prereq: [One yr calculus, PHYS 1302] or instr consent
BBE 3013 - Engineering Principles of Molecular and Cellular Processes
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall
Applied engineering principles in biological processes. Classification of microbes of industrial importance. Parameters for cellular control. Modeling of cell growth/metabolism, enzymatic catalysis, bioreactor design, product recovery operations design. Case studies.
prereq: BIOL 1009 or BIOL 2003; and CHEM 1062/CHEM 1066 or equivalent or CHEM 1082; MATH 1372 or MATH 1282
BIOC 3021 - Biochemistry
Credits:
3.0
[max 3.0]
Course Equivalencies:
BioC 3021/BioC 3022/BioC 4331/
Typically offered:
Every Fall, Spring & Summer
Fundamentals of biochemistry. Structure/function of nucleic acids, proteins, lipids, carbohydrates. Enzymes. Metabolism. DNA replication and repair, transcription, protein synthesis.
Recommended prerequisites: Introductory biology (BIOL 1009 or BIOL 2003 or equivalent), organic chemistry (CHEM 2301 or CHEM 2081/2085 or equivalent).
Note: CBS students should take BIOC 3022 not 3021.
BMEN 5101 - Advanced Bioelectricity and Instrumentation
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Instrumentation, computer systems, and processing requirements for clinical physiological signals. Electrode characteristics, signal processing, and interpretation of physiological events by ECG, EEG, and EMG. Measurement of respiration and blood volume/flow.
prereq: [CSE upper div, grad student] or instructor consent
BMEN 5111 - Biomedical Ultrasound
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Introduction to biomedical ultrasound, including physics of ultrasound, transducer technology, medical ultrasound imaging, photoacoustic imaging, applications of non-linear acoustics, and high-intensity ultrasound.
prereq: [[PHYS 1302 or equiv], [MATH 2374 or equiv]] or instr consent
BMEN 5151 - Introduction to BioMEMS and Medical Microdevices
Credits:
2.0
[max 2.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Design/microfabrication of sensors, actuators, drug delivery systems, microfluidic devices, and DNA/protein microarrays. Packaging, biocompatibility, ISO 10993 standards. Applications in medicine, research, and homeland security.
prereq: CSE sr or grad student or medical student
BMEN 5401 - Advanced Biomedical Imaging
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall
Functional biomedical imaging modalities. Principles/applications of technologies that offer high spatial/temporal resolution. Bioelectromagnetic and magnetic resonance imaging. Other modalities.
prereq: CSE upper div or grad student or instr consent
BMEN 5411 - Neural Engineering
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus.
prereq: 3401 recommended
BMEN 5412 - Neuromodulation
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall
Fundamentals of bioengineering approaches to modulate the nervous system, including bioelectricity, biomagnetism, and optogenetics. Computational modeling, design, and physiological mechanisms of neuromodulation technologies. Clinical exposure to managing neurological disorders with neuromodulation technology.
BMEN 5421 - Introduction to Biomedical Optics
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Periodic Spring
Biomedical optical imaging/sensing principles, laser-tissue interaction, detector design, noise analysis, interferometry, spectroscopy. Optical coherence tomography, polarization, birefringence, flow measurement, fluorescence, nonlinear microscopy. Tours of labs.
prereq: CSE sr or grad student
CEGE 3501 - Introduction to Environmental Engineering
(ENV)
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
A quantitative approach to environmental problems, including the development of mass and energy balances and the application of fundamental principles of environmental chemistry and microbiology. Meets the University of Minnesota's liberal education environment theme through the incorporation of environmental function, problems, and solutions throughout the course.
prereq: Chem 1062, Phys 1302, Math 1372 or equivalent
CEGE 3502 - Fluid Mechanics
Credits:
4.0
[max 4.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Fluid statics/dynamics. Kinematics of fluid flow, equations of motion, pressure-velocity relationships, viscous effects, boundary layers. Momentum/energy equations. Lift/drag. Flow in pipes and pipe systems. Hydraulic machinery. Fluid measurements.
prereq: [AEM 2012 or AEM 3031], Math 2373, CEGE 3101
CHEM 1062 - Chemical Principles II
(PHYS)
Credits:
3.0
[max 3.0]
Course Equivalencies:
Chem 1062/1072/1072H/1082/
Typically offered:
Every Fall, Spring & Summer
Chemical kinetics. Radioactive decay. Chemical equilibrium. Solutions. Acids/bases. Solubility. Second law of thermodynamics. Electrochemistry/corrosion. Descriptive chemistry of elements. Coordination chemistry. Biochemistry.
prereq: Grade of at least C- in 1061 or equiv, concurrent registration is required (or allowed) in 1066; registration for 1066 must precede registration for 1062
CHEM 1066 - Chemical Principles II Laboratory
(PHYS)
Credits:
1.0
[max 1.0]
Course Equivalencies:
Chem 1066/Chem 1076H
Grading Basis:
A-F only
Typically offered:
Every Fall, Spring & Summer
Basic laboratory skills while investigating physical and chemical phenomena closely linked to lecture material. Experimental design, data collection and treatment, discussion of errors, and proper treatment of hazardous wastes.
prereq: concurrent registration is required (or allowed) in 1062
CHEM 2301 - Organic Chemistry I
Credits:
3.0
[max 3.0]
Course Equivalencies:
Chem 2301/Chem 2331H
Typically offered:
Every Fall, Spring & Summer
Organic compounds, constitutions, configurations, conformations, reactions. Molecular structure. Chemical reactivity/properties. Spectroscopic characterization of organic molecules.
prereq: C- or better in 1062/1066 or 1072H/1076H
CHEM 2302 - Organic Chemistry II
Credits:
3.0
[max 3.0]
Course Equivalencies:
Chem 2302/Chem 2332HChem 2304
Prerequisites:
Grade of at least C- in 2301
Typically offered:
Every Fall, Spring & Summer
Reactions, synthesis, and spectroscopic characterization of organic compounds, organic polymers, and biologically important classes of organic compounds such as lipids, carbohydrates, amino acids, peptides, proteins, and nucleic acids.
prereq: Grade of at least C- in 2301
CHEM 2311 - Organic Lab
Credits:
4.0
[max 4.0]
Course Equivalencies:
Chem 2311/Chem 2312H/2312
Typically offered:
Every Fall, Spring & Summer
Laboratory techniques in synthesis, purification and characterization of organic compounds with an emphasis on green chemistry methodologies.
prereq: Grade of at least C- in [2302] or [concurrent registration is required (or allowed) in 2302
CHEM 2331H - Honors Elementary Organic Chemistry I
Credits:
3.0
[max 3.0]
Course Equivalencies:
Chem 2301/Chem 2331H
Grading Basis:
A-F only
Typically offered:
Every Fall
Important classes of organic compounds, their constitutions, configurations, conformations, reactions. Relationships between molecular structure/chemical properties/reactivities. Spectroscopic methods/characterization of organic molecules.
prereq: At least B+ in 1072H, UHP student
CHEM 4501 - Introduction to Thermodynamics, Kinetics, and Statistical Mechanics
Credits:
3.0
[max 3.0]
Course Equivalencies:
Chem 3501/4501
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Physical chemistry as it relates to macroscopic descriptions of chemical systems. Chemical thermodynamics, phase equilibria, chemical equilibria. Statistical mechanics. Phenomenological reaction kinetics. Kinetic theory
of gases. Collision, statistical theories of reaction rates.
prereq: [1062/1066 or 1071H/1075H], [MATH 2263 or concurrent registration is required (or allowed) in MATH 2263 or MATH 2374 or concurrent registration is required (or allowed) in MATH 2374], [PHYS 1302 or PHYS 1402V or PHYS 1502V]
CHEM 4502 - Introduction to Quantum Mechanics and Spectroscopy
Credits:
3.0
[max 3.0]
Course Equivalencies:
Chem 3502/4502
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Microscopic descriptions of chemical systems. Quantum theory. Applications to atomic/molecular structure. Molecular spectroscopy. Quantum statistical mechanics. Discussion of solutions to several differential equations.
prereq: [1062/1066 or 1072H/1076H of 1082/1086], [MATH 2263 or concurrent registration is required (or allowed) in MATH 2263 or MATH 2374 or concurrent registration is required (or allowed) in MATH 2374 or MATH 2243 or concurrent registration is required (or allowed) in MATH 2243 or MATH 2373 or concurrent registration is required (or allowed) in MATH 2373], [PHYS 1302 or PHYS 1402V or PHYS 1502V]
EE 2701 - Sustainable Electricity Supply: Renewables and Conservation
(TS)
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
This course is on the very timely topic of combating climate change by looking closely at electricity generation, delivery, and its use for a sustainable future. Generating electricity from renewables and conservation in all forms, including improving energy efficiency, are the most important tools we have for combating climate change. This course will help you understand the historical development of energy production, the economic impacts of energy sources, the political implications, and primarily the technical understanding of solar power, wind power, electric vehicles, battery storage, fuel cells, energy distribution, and conservation. It will help you consider the potential societal benefits such as reduced energy bills, cleaner air and water, increased economic opportunities, and prepare you for exciting and meaningful careers in renewable energy and sustainability.
Prerequisite: Physics 1302W (or equivalent)
EE 2703 - Sustainable Electricity Supply: Renewables and Conservation Lab
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
This online lab is to complement what students are learning in the associated three-credit course EE2701. Students will conduct experiments related to Wind Turbines, Electronic Converters, Photovoltaics, LEDs, and the Smart Grid. Since all the experiments are digitally controlled, they can easily be performed online. Co-requisite: EE2701
GCC 3011 - Pathways to Renewable Energy
(TS)
Credits:
3.0
[max 3.0]
Course Equivalencies:
GCC 3011/GCC 5011
Grading Basis:
A-F only
Typically offered:
Periodic Spring
This interdisciplinary course will examine obstacles to energy transitions at different scales. It will explore the role of energy in society, the physics of energy, how energy systems were created and how they function, and how the markets, policies, and regulatory frameworks for energy systems in the US developed. The course will closely examine the Realpolitik of energy and the technical, legal, regulatory, and policy underpinnings of renewable energy in the US and Minnesota. Students will learn the drivers that can lead global systems to change despite powerful constraints and how local and institutional action enables broader reform. Students will put their learning into action by developing a proposal and then working on a project to accelerate the energy transition and to ensure that the energy transition benefits people in a just and equitable way.
This is a Grand Challenge Curriculum course.
prereq: sophomore, junior, senior
GCC 3027 - Power Systems Journey: Making the Invisible Visible and Actionable
(TS)
Credits:
3.0
[max 3.0]
Course Equivalencies:
GCC 3027/GCC 5027
Grading Basis:
A-F only
Typically offered:
Periodic Fall
An energy revolution is underway, and needs to accelerate to support climate and economic goals. But the general citizenry does not understand our current energy systems, particularly the seemingly invisible phenomena of electricity, and its generation, distribution, and use. Technical knowledge is only half the solution, however. It is through human decisions and behaviors that technical solutions get applied and adopted, and the importance of communication and storytelling is being recognized for its relevance to making change. How can science literacy and behavior-motivating engagement and storytelling be combined to help make systemic change? This course explores the integration of science-based environmental education, with art-led, place-based exploration of landscapes and creative map-making to address this challenge. How do we make electricity visible, understandable, and interesting -- so we can engage citizens in energy conservation with basic literacy about the electric power system so that they can be informed voters, policy advocates, and consumers. In this class, you will take on this challenge, first learning about the electric power systems you use, their cultural and technical history, systems thinking, design thinking, and prior examples of communication and education efforts. With this foundation, you will then apply your learning to create a public education project delivered via online GIS Story maps that use a combination of data, art, and story to help others understand, and act on the power journey we are all on. All will share the common exploration of power systems through field trips, and contribute to a multi-faceted story of power, presented in a group map and individual GIS Story maps. No prior knowledge of GIS story maps or electricity issues is needed. The study of power systems can be a model for learning and communicating about other topics that explore the interaction of technology and society toward sustainability.
This is a Grand Challenge Curriculum course. GCC courses are open to all students and fulfill an honors experience for University Honors Program students.
GCC 5011 - Pathways to Renewable Energy
(TS)
Credits:
3.0
[max 3.0]
Course Equivalencies:
GCC 3011/GCC 5011
Grading Basis:
A-F only
Typically offered:
Periodic Spring
This interdisciplinary course will examine obstacles to energy transitions at different scales. It will explore the role of energy in society, the physics of energy, how energy systems were created and how they function, and how the markets, policies, and regulatory frameworks for energy systems in the US developed. The course will closely examine the Realpolitik of energy and the technical, legal, regulatory, and policy underpinnings of renewable energy in the US and Minnesota. Students will learn the drivers that can lead global systems to change despite powerful constraints and how local and institutional action enables broader reform. Students will put their learning into action by developing a proposal and then working on a project to accelerate the energy transition and to ensure that the energy transition benefits people in a just and equitable way.
This is a Grand Challenge Curriculum course.
GCC 5027 - Power Systems Journey: Making the Invisible Visible and Actionable
(TS)
Credits:
3.0
[max 3.0]
Course Equivalencies:
GCC 3027/GCC 5027
Grading Basis:
A-F only
Typically offered:
Periodic Fall
An energy revolution is underway, and needs to accelerate to support climate and economic goals. But the general citizenry does not understand our current energy systems, particularly the seemingly invisible phenomena of electricity, and its generation, distribution, and use. Technical knowledge is only half the solution, however. It is through human decisions and behaviors that technical solutions get applied and adopted, and the importance of communication and storytelling is being recognized for its relevance to making change. How can science literacy and behavior-motivating engagement and storytelling be combined to help make systemic change? This course explores the integration of science-based environmental education, with art-led, place-based exploration of landscapes and creative map-making to address this challenge. How do we make electricity visible, understandable, and interesting--so we can engage citizens in energy conservation with basic literacy about the electric power system so that they can be informed voters, policy advocates, and consumers. In this class, you will take on this challenge, first learning about the electric power systems you use, their cultural and technical history, systems thinking, design thinking, and prior examples of communication and education efforts. With this foundation, you will then apply your learning to create a public education project delivered via online GIS Story maps that use a combination of data, art, and story to help others understand, and act on the power journey we are all on. All will share the common exploration of power systems through field trips, and contribute to a multi-faceted story of power, presented in a group map and individual GIS Story maps. No prior knowledge of GIS story maps or electricity issues is needed. The study of power systems can be a model for learning and communicating about other topics that explore the interaction of technology and society toward sustainability.
This is a Grand Challenge Curriculum course. GCC courses are open to all students and fulfill an honors experience for University Honors Program students.
IE 5111 - Systems Engineering I
Credits:
2.0
[max 2.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall
Overview of systems-level thinking/techniques in context of an integrated, design-oriented framework. Elements of systems engineering process, including lifecycle, concurrent, and global engineering. Framework for engineering large-scale, complex systems. How specific techniques fit into framework.
prereq: CSE upper div or grad student
IE 5113 - Systems Engineering II
Credits:
4.0
[max 4.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Systems engineering thinking/techniques presented in 5111. Hands-on techniques applied to specific problems. Topics pertinent to effectiveness of design process. Practices and organizational/reward structure to support collaborative, globally distributed design team.
IE 5441 - Financial Decision Making
Credits:
4.0
[max 4.0]
Grading Basis:
A-F only
Typically offered:
Every Fall, Spring & Summer
Cash flow streams, interest rates, fixed income securities. Evaluating investment alternatives, capital budgeting, dynamic cash flow process. Mean-variance portfolio selection, Capital Asset Pricing Model, utility maximization, risk aversion. Derivative securities, asset dynamics, basic option pricing theory.
prereq: CSE upper div or grad student
IE 5511 - Human Factors and Work Analysis
Credits:
4.0
[max 4.0]
Course Equivalencies:
HumF 5211/IE 5511/ME 5211
Grading Basis:
A-F or Aud
Typically offered:
Every Fall
Human factors engineering (ergonomics), methods engineering, and work measurement. Human-machine interface: displays, controls, instrument layout, and supervisory control. Anthropometry, work physiology and biomechanics. Work environmental factors: noise, illumination, toxicology. Methods engineering, including operations analysis, motion study, and time standards.
prereq: Upper div CSE or grad student
IE 5513 - Engineering Safety
Credits:
4.0
[max 4.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Occupational, health, and product safety. Standards, laws, and regulations. Hazards and their engineering control, including general principles, tools and machines, mechanics and structures, electrical safety, materials handling, fire safety, and chemicals. Human behavior and safety, procedures and training, warnings and instructions.
prereq: Upper div CSE or grad student
IE 5522 - Quality Engineering and Reliability
Credits:
4.0
[max 4.0]
Course Equivalencies:
IE 3522/IE 5522
Typically offered:
Periodic Fall & Spring
Quality engineering/management, economics of quality, statistical process control design of experiments, reliability, maintainability, availability.
prereq: [4521 or equiv], [upper div or grad student or CNR]
IE 5531 - Engineering Optimization I
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning.
prereq: Upper div or grad student or CNR
IE 5541 - Project Management
Credits:
4.0
[max 4.0]
Course Equivalencies:
IE 4541/IE 5541
Grading Basis:
A-F only
Typically offered:
Every Fall & Spring
Introduction to engineering project management. Analytical methods of selecting, organizing, budgeting, scheduling, and controlling projects, including risk management, team leadership, and program management.
prereq: Upper div or grad student
IE 5551 - Production and Inventory Systems
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Inventory control, supply chain management, demand forecasting, capacity planning, aggregate production and material requirement planning, operations scheduling, and shop floor control. Quantitative models used to support decisions. Implications of emerging information technologies and of electronic commerce for supply chain management and factory operation.
prereq: CNR or upper div or grad student
IE 5553 - Simulation
Credits:
4.0
[max 4.0]
Course Equivalencies:
IE 3553/IE 5553
Typically offered:
Periodic Fall & Spring
Discrete event simulation. Using integrated simulation/animation environment to create, analyze, and evaluate realistic models for various industry settings, including manufacturing/service operations and systems engineering. Experimental design for simulation. Selecting input distributions, evaluating simulation output.
prereq: Upper div or grad student; familiarity with probability/statistics recommended
INET 4021 - Dev Ops I: Network Programming
Credits:
4.0
[max 4.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Network and distributed programming concepts. Design using C, Java, and other higher-level programming languages. Sockets, TCP/IP, RPC, streaming, CORBA, .NET, and SOAP. Labs use UNIX/Linux and MS Windows operating systems.
prereq: major admission requirements completed.
MATH 3283W - Sequences, Series, and Foundations: Writing Intensive
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Math 2283/3283W
Typically offered:
Every Fall & Spring
Introduction to reasoning used in advanced mathematics courses. Logic, mathematical induction, real number system, general/monotone/recursively defined sequences, convergence of infinite series/sequences, Taylor's series, power series with applications to differential equations, Newton's method. Writing-intensive component.
prereq: [concurrent registration is required (or allowed) in 2243 or concurrent registration is required (or allowed) in 2263 or concurrent registration is required (or allowed) in 2373 or concurrent registration is required (or allowed) in 2374] w/grade of at least C-
MATS 3011 - Introduction to Materials Science and Engineering
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall & Spring
Builds progressively from electrons to atoms to bonding to crystal structures. Defects, X-ray diffraction, phase diagrams. Microstructure as basis for understanding mechanical/electrical properties. Metals, polymers, ceramics, semiconductors, composites.
prereq: CHEM 1061, CHEM 1065, [MATH 1272 or MATH 1372], PHYS 1302, CSE student
MATS 3012 - Metals and Alloys
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall
Structure of metals/alloys. Crystal structure/defects (point defects, dislocations, grain boundaries). Microstructure. Properties of metals, especially mechanical properties.
prereq: [3011, [MatS or ChEn upper div]] or instr consent
MATS 3013 - Electrical and Magnetic Properties of Materials
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall
Electronic/magnetic properties of solids. Simple band theory of solids. Free electron theory of conductivity/transport. Optical/dielectric response functions. Elementary theory of magnetism. Electronic devices. Superconductivity. Computer-based problems to illustrate applications.
prereq: 3011, [CHEM 4502 or PHYS 2303], [upper div MatS or ChEn] or instr consent
MATS 3851W - Materials Properties Lab
(WI)
Credits:
4.0
[max 4.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Characterization of properties of engineering materials. Mechanical, electrical, optical, magnetic, and thermal properties. Relationship between properties and materials structure. Specimen preparation. Data collection and analysis, including statistical analysis. Laboratory notebook and report writing.
prereq: [3801, 3013, MatS upper div] or dept consent
ME 3324 - Introduction to Thermal Science
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Periodic Fall & Spring
Thermodynamics, heat transfer. Thermal properties of substances. First/second laws of thermodynamics. Steady/unsteady heat conduction. Thermal resistance concept. Convection heat transfer. Radiative heat transfer between solid surfaces. Boiling/condensation heat transfer.
prereq: Chem 1061, Chem 1065, Math 2243 or Math 2373, Phys 1301, [CSE student]
ME 3331 - Thermodynamics
Credits:
3.0
[max 3.0]
Course Equivalencies:
ME 3321/ME 3331
Grading Basis:
A-F only
Typically offered:
Every Fall, Spring & Summer
Properties, equations of state, processes, cycles for reversible and irreversible thermodynamic systems. Modes of energy transfer. Equations for conservation of mass, energy, entropy balances. Application of thermodynamic principles to modern engineering systems.
prereq: Chem 1061, Chem 1065, Phys 1301
ME 3332 - Fluid Mechanics
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Every Fall, Spring & Summer
Mass, momentum conservation principles. Fluid statics, Bernoulli equation. Control volume analysis, dimensional analysis, internal and external viscous flow. Momentum and energy considerations. Introduction to boundary layers.
prereq: Math 2243 or Math 2373, 3331
ME 3333 - Heat Transfer
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Every Fall, Spring & Summer
Mechanisms of heat transfer. Conduction, convection, radiation. Boundary layer analysis using momentum and energy equations. Applications such as fins, heat exchangers, electronics cooling, bioheat transfer, energy conversion technologies, phase change energy storage and boiling.
prereq: 3332
PHSL 3061 - Principles of Physiology
Credits:
4.0
[max 4.0]
Course Equivalencies:
Phsl 3063/Phsl 3071
Typically offered:
Every Fall
Human physiology with emphasis on quantitative aspects. Organ systems (circulation, respiration, gastrointestinal, renal, endocrine, muscle, peripheral and central nervous systems), cellular transport processes, and scaling in biology.
prereq: 1 year college chem and physics and math through integral calculus
PHYS 2303 - Physics III: Physics of Matter
Credits:
4.0
[max 4.0]
Course Equivalencies:
Phys 2303/2403H/2503/2503H
Typically offered:
Every Spring
Thermodynamics, mechanical/electromagnetic waves, optics, quantum theory. Applications of quantum nature of solids.
prereq: 1302, [MATH 1272 or MATH 1372 or MATH 1572H], [MatSci or EE] student
PHYS 2503 - Physics III: Intro to Waves, Optics, and Special Relativity
Credits:
4.0
[max 4.0]
Course Equivalencies:
Phys 2303/2403H/2503/2503H
Typically offered:
Every Fall
Third semester of introductory physics. Mechanical/electromagnetic waves, optics, special relativity.
prereq: 1302W or equivalent
PHYS 2503H - Honors Physics III
Credits:
4.0
[max 4.0]
Course Equivalencies:
Phys 2303/2403H/2503/2503H
Grading Basis:
A-F only
Typically offered:
Every Fall
The third semester of a calculus-based introductory physics sequence. Topics include: relativistic kinematics and dynamics, mechanical and electromagnetic waves, light, interference, diffraction, wave-particle duality, and topics in modern physics. Course emphasizes the use of fundamental problems to solve quantitative problems. Intended primarily for those who have completed 1401V/1402V, although those students with outstanding performance in 1301W/1302W may be granted permission to enroll.
Prereq: Honors program or with permission, PHYS 1402V or equivalent
PHYS 2601 - Quantum Physics
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Introduction to quantum mechanics. Applications to atomic, molecular, condensed-matter, nuclear, elementary-particle, and statistical physics.
Prereq: PHYS2503/2503H, Recommended Concurrent: Phys 3041
PHYS 4101 - Quantum Mechanics
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Mathematical techniques of quantum mechanics. Schrodinger Equation and simple applications. General structure of wave mechanics. Operator methods, perturbation theory, radiation from atoms.
Prereq: PHYS 3041, PHYS 2601
PHYS 4201 - Statistical and Thermal Physics
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Principles of thermodynamics and statistical mechanics. Selected applications such as kinetic theory, transport theory, and phase transitions.
Prereq: PHYS 3041, PHYS 2201, PHYS 2601
STAT 5101 - Theory of Statistics I
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution.
prereq: (MATH 2263 or MATH 2374 or MATH 2573H), (MATH 2142 or CSCI 2033 or MATH 2373 or MATH 2243)
STAT 5102 - Theory of Statistics II
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory.
prereq: [5101 or Math 5651 or instr consent]
CSE 4896 - Cooperative Industrial Assignment I
Credits:
2.0
[max 2.0]
Grading Basis:
A-F only
Typically offered:
Every Spring & Summer
This course accompanies an industrial work assignment in engineering and applied science. It includes analysis of technical problems that require developing criteria, evaluating alternatives, and completing a final analysis. A final technical design report emphasizes design communication and describes the technical decision process, analysis, and final recommendations.
This course is intended for any College of Science and Engineering student who has been admitted to their major and is participating in the co-op program. There are no specific prerequisites for this course, though it is expected that students taking this course have a background in science or engineering appropriate for their industrial co-op position.
Registration is by permission only. Please submit your application to the Co-op Program via Handshake to initiate the course access process. Detailed instructions can be found on the co-op website (https://cse.umn.edu/coop/application-process).
This is the first course in a two-semester sequence. CSE 4896 is offered in the summer and spring semesters. CSE 4996 is offered in the Summer and fall semesters. It is expected that students complete a co-op experience back-to-back (i.e. summer/fall or spring/summer). If your co-op plan differs from that, please email co-op@umn.edu to receive permission to take the courses in reverse order.
CSE 4996 - Cooperative Industrial Assignment II
Credits:
2.0
[max 2.0]
Grading Basis:
A-F only
Typically offered:
Every Fall & Summer
This course accompanies an industrial work assignment in engineering and applied science. The course includes generation of a novel idea requiring developing criteria, evaluating alternatives, and completing a final analysis. A final invention disclosure report emphasizes innovation and communication and includes the technical creation process, hypothesis testing, analysis, and final recommendations. This is the second course in a two course series. While some content is similar between the courses, the first course focuses more on the design and problem solving in an industrial context, this course focuses on the idea creation process and intellectual property protection. This course also includes a section on diversity, equity and inclusion in corporate settings.
This course is intended for any College of Science and Engineering student who has been admitted to their major and is participating in the CSE Co-op Program. There are no specific prerequisites for this course, though it is expected that students taking this course have a background in science or engineering appropriate for their industrial co-op position.
Registration is by permission only. Please submit your application to the Co-op Program via Handshake to initiate the course access process. Detailed instructions can be found on the co-op website (https://cse.umn.edu/coop/application-process).
This is the second course in a two semester sequence. CSE 4896 is offered in the summer and spring semesters. CSE 4996 is offered in the summer and fall semesters. It is expected that students complete a co-op experience back-to-back (ie. summer/fall or spring/summer). If your co-op plan differs from that, please email co-op@umn.edu to receive permission to take the courses in reverse order.
Team projects based on commercializable technologies or innovations. Teams present their ideas to investors and industry professionals. Students are encouraged to submit their business plans to Minnesota Cup.
MOT 4001 - Leadership, Professionalism and Business Basics for Engineers
Credits:
2.0
[max 2.0]
Grading Basis:
A-F only
Typically offered:
Every Fall & Spring
Elements of business, environment in which technology/business operate. Classes of 15 to 20 students.
EE 4981H - Senior Honors Project I
Credits:
2.0
[max 2.0]
Typically offered:
Every Fall
Experience in research/design for electrical/computer engineering. Oral/written reports.
prereq: ECE honors, sr, instr consent
EE 4982V - Senior Honors Project II
(WI)
Credits:
2.0
[max 2.0]
Typically offered:
Every Spring
Experience in research/design for electrical/computer engineering. Oral/written reports.
prereq: 4981
ACCT 3001 - Strategic Management Accounting
Credits:
3.0
[max 3.0]
Course Equivalencies:
Acct 3001/IBus 3002
Grading Basis:
A-F or Aud
Typically offered:
Every Fall, Spring & Summer
Costing techniques, including activity-based costing. Applying costing methods to determine costs of products, services, and production processes. Use of costs in operating/strategic decisions.
prereq: ACCT 2051 or 2050
FINA 3001 - Finance Fundamentals
Credits:
3.0
[max 3.0]
Course Equivalencies:
ApEc 3501/Fina 3001/Fina 3001H
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
How competition for capital in Capital Markets establishes metrics and measures used to understand financial performance of the firm. The course introduces the finance view of the firm and the application of value creation principles to firm decision making. Course presents the centrality of cash flows, the theoretical foundations for Time Value of Money, decision tools for investment of capital, basic valuation of stocks and bonds, and the theoretical foundations for the impact of risk on the required return on investor capital.
prereq: ACCT 2050 or ACCT 2051, SCO 2550 or BA 2551 or equivalent statistics course
HRIR 3021 - Human Capital Management
Credits:
3.0
[max 3.0]
Course Equivalencies:
HRIR 3021/HRIR 3021H/IBUS 3021
Grading Basis:
A-F only
Typically offered:
Every Fall, Spring & Summer
This course will focus on the people side of business. We will look at how, through managing and leading people, we can create an engaged, productive workforce in order to achieve organizational strategic objectives. The content of this course is complementary to any major or minor.
Major topics in this course:
- Managing people in an ethical, legal way that is aligned with corporate strategy and helps organizations reach their goals;
- Successfully attracting, recruiting, and selecting talented people;
- Creating interesting, engaging jobs and giving meaningful feedback in order to retain great employees;
- Rewarding and motivating people through intrinsic and extrinsic methods to encourage the most effective and "right" kind of employee behaviors to create an engaged, productive workforce through people strategies and practices.
IDSC 3001 - Information Systems & Digital Transformation
(TS)
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall, Spring & Summer
Information technologies have transformed the way businesses operate and the way consumers interact with businesses. They have enabled organizations to increase efficiency, reduce costs, and reach new customers. Their impact goes beyond the business world and affects nearly every aspect of modern society. Along with the benefits they provide, technologies have created new problems around privacy, security, misinformation on social media, algorithmic bias, and potential stifling of competition and innovation. In today's digital age, it is crucial to develop an understanding of information technologies, their impact on business and society, and the challenges they pose for decision making in commercial firms, government agencies, and public policies.
This course is designed to cover a broad range of information technology issues in order to prepare students for the knowledge intensive economy of the 21st century. Students will be exposed to not only the technical aspects of information technologies, but also the social, political, and economic factors that shape its development and use. Through a combination of lectures, discussions, videos, in-class exercises and talks by guest speakers, students will gain an in-depth understanding of how information technologies are shaping businesses and the society as a whole. Students will also develop critical thinking skills to analyze and evaluate the impact of technology on society.
Topics include business strategy and disruptive technologies, enterprise systems such as those for Customer Relationship Management, Supply Chain Management and Human Resource Management, electronic and mobile commerce, social media applications and their social impact, cloud computing, data analytics, IT privacy and security, artificial intelligence and its social impact.
MGMT 3001 - Fundamentals of Management
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall, Spring & Summer
This course is about the foundational principles of management, encompassing disciplinary and topical boundaries. We will look at these principles from the perspective of how they guide action, specifically: planning, organizing, leading and controlling. By the end of the course, students will know the basics of how to set up organizations to be effective and innovative, and not just efficient. During the course, you will engage with the material in the course and understand how management frameworks can be used to choose the right internal structures and processes that can best react to your particular industry context and general business environment.
MGMT 3015 - Introduction to Entrepreneurship
Credits:
4.0
[max 4.0]
Course Equivalencies:
IBUS 3010/MGMT 3010/MGMT 3015
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Fundamentals of entrepreneurship. Career paths, including new business start-ups, franchising, acquisitions (including family business succession), corporate venturing, and entre-preneurial services. Legal structures for new business formation. Aspects of business law/ethics.
MKTG 3001 - Principles of Marketing
Credits:
3.0
[max 3.0]
Course Equivalencies:
Mktg 3001/Mktg 3001H
Grading Basis:
A-F or Aud
Typically offered:
Every Fall, Spring & Summer
Introduction to terms, concepts, and skills for analyzing marketing problems. Factors outside the organization affecting its product, pricing, promotion, and distribution decisions. Cases from actual organizations.
prereq: ECON 1101 or ECON 1165
PA 3003 - Nonprofit and Public Financial Management
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Concepts/tools for project/budget planning. Program analysis. Interpreting financial reports. Identifying/resolving organizational performance issues. Case studies, real-world exercises.
prereq: Jr or sr
PA 4101 - Nonprofit Management and Governance
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall & Spring
Managing/governing nonprofit/public organizations. Theories, concepts, real-world examples. Governance systems, strategic management practices, effect of different funding environments, management of multiple constituencies.
SCO 3001 - Sustainable Supply Chain and Operations
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Sustainable Supply Chain and Operations Management focuses on the design and management of transformation processes to provide products and services to create value for the people, planet, and firm prosperity. On the one hand, supply chain and operations management involves the integration of activities and processes, to facilitate the flows of materials, services, finances, and information to convert inputs into the firms? primary products and services. Operational issues include the design of products and processes, the procurement of raw materials, the control of inventories, the maintenance of quality, the planning of human resources and facilities, and the delivery of products or services, so that customer expectations and needs are met. Operations also have significant interactions with other functional areas of the firm (e.g., finance, marketing, strategy, and accounting). Therefore, understanding the role of the operations function and its impact on the competitiveness of the firm from both tactical and strategic aspects is an important part of any manager's training. This course will introduce students to the fundamental concepts, operations practices, and models in both manufacturing- and service-oriented firms. The course will cover both quantitative and qualitative methods.
EE 4951W - Senior Design Project
(WI)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Team participation in formulating/solving open-ended design problems. Oral/written presentations.
prereq: 3015, 3115, 3102, attendance first day of class
EE 4111 - Advanced Analog Electronics Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Basic integrated circuit building blocks of differential amplifiers, high bandwidth, instrumentation amplifiers. Current/voltage references. Feedback, stability, and noise in electronic circuits. Integral lab.
prereq: 3015, 3115
EE 4163 - Energy Conversion and Storage Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Provides laboratory experiences with the topics of 4161W, including the fundamental physics and chemistry of selected energy conversion and energy storage devices, their application, and their connection strategies in electric power applications.
prereq: concurrent registration is required (or allowed) in 4161W
EE 4235 - Linear Control Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Lab to accompany 4231.
prereq: 4231 or concurrent registration is required (or allowed) in 4231
EE 4237 - State Space Control Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Lab to accompany 4233.
prereq: 4233 or concurrent registration is required (or allowed) in 4233; no cr for [EE or CompE] grad students
EE 4301 - Digital Design With Programmable Logic
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Summer
Introduction to system design/simulation. Design using Verilog code/synthesis. Emulation using Verilog code.
prereq: 2301, [1301 or CSCI 1113 or CSCI 1901]
EE 4341 - Embedded System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microcontroller interfacing for embedded system design. Exception handling/interrupts. Memory Interfacing. Parallel/serial input/output methods. System Buses and protocols. Serial Buses and component interfaces. Microcontroller Networks. Real-Time Operating Systems. Integral lab.
prereq: 2301, 2361, upper div CSE
EE 4505 - Communications Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Experiments in analysis/design of wired/wireless communication systems. Lab to accompany 4501.
prereq: 4501 or concurrent registration is required (or allowed) in 4501
EE 4703 - Electric Drives Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Laboratory to accompany 4701. Simulink-based simulations of electric machines/drives in applications such as energy conservation and motion control in robotics.
prereq: 4701 or concurrent registration is required (or allowed) in 4701
EE 4722 - Power System Analysis Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Lab analysis of AC power systems, power system networks, power flow, short circuit, transient stability.
prereq: 4721 or concurrent registration is required (or allowed) in 4721
EE 4743 - Switch-Mode Power Electronics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Laboratory to accompany 4741. PSpice-/Simulink-based simulations of converters, topologies, and control in switch-mode dc power supplies, motor drives for motion control, and inverters for interfacing renewable energy sources to utility grid.
prereq: 4741 or concurrent registration is required (or allowed) in 4741
EE 4930 - Special Topics in Electrical and Computer Engineering Laboratory
Credits:
1.0
-2.0
[max 6.0]
Grading Basis:
A-F only
Typically offered:
Periodic Fall, Spring & Summer
Lab work not available in regular courses. Topics vary.
prereq: CSE sr or grad student or instr consent
EE 5141 - Introduction to Microsystem Technology
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microelectromechanical systems composed of microsensors, microactuators, and electronics integrated onto common substrate. Design, fabrication, and operation principles. Labs on micromachining, photolithography, etching, thin film deposition, metallization, packaging, and device characterization.
prereq: [3161, 3601, CSE grad student] or dept consent
EE 5173 - Basic Microelectronics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Students fabricate a polysilicon gate, single-layer metal, NMOS chip, performing 80 percent of processing, including photolithography, diffusion, oxidation, and etching. In-process measurement results are compared with final electrical test results. Simple circuits are used to estimate technology performance.
prereq: [[5171 or concurrent registration is required (or allowed) in 5171], CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation.
prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5373 - Data Modeling Using R
Credits:
1.0
[max 1.0]
Grading Basis:
A-F only
Typically offered:
Periodic Fall & Spring
Introduction to data modeling and the R language programming. Multi-factor linear regression modeling.
Residual analysis and model quality evaluation. Response prediction. Training and testing. Integral lab.
An introductory course in probability and statistics is suggested but not required; basic programming skills in some high-level programming language, such as C/C++, Java, Fortran, etc also suggested.
EE 5545 - Digital Signal Processing Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Real-time implementation of digital signal processing (DSP) algorithms, including filtering, sample-rate conversion, and FFT-based spectral analysis. Implementation on a modern DSP Platform. Processor architecture. Arithmetic operations. Real-time processing issues. Processor limitations. Integral laboratory.
prereq: [4541, CSE grad student] or dept consent
EE 5613 - RF/Microwave Circuit Design Laboratory
Credits:
2.0
[max 2.0]
Grading Basis:
A-F only
Typically offered:
Every Spring
Scattering parameters, planar lumped circuits, transmission lines, RF/microwave substrate materials, matching networks/tuning elements, resonators, filters, combiners/dividers, couplers. Integral lab.
prereq: [[5601 or concurrent registration is required (or allowed) in 5601], CSE grad student] or dept consent
EE 5622 - Physical Optics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Fundamental optical techniques. Diffraction and optical pattern recognition. Spatial/temporal coherence. Interferometry. Speckle. Coherent/incoherent imaging. Coherent image processing. Fiber Optics.
prereq: [[5621 or concurrent registration is required (or allowed) in 5621], CSE grad student] or dept consent
EE 5657 - Physical Principles of Thin Film Technology
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Fabrication, characterization, and application of thin film and nanostructured materials and devices. Focuses on vacuum deposition. Materials science. Hands-on, team-based labs.
EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Periodic Spring
Lab to accompany 5705.
prereq: 5705 or concurrent registration is required (or allowed) in 5705
EE 5811 - Biological Instrumentation
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
This course will cover the physics and technology of biological instruments. The operating principles of optical, electrical, and mechanical biosensors will be discussed, followed by transport and delivery of biomolecules to the sensors. Techniques to manufacture these sensing devices, along with microfluidic packaging, will be covered. Lectures will be complemented by lab demo sessions to give students hands-on experiences in microfluidic chip fabrication, microscopy, and particle trapping experiments.
CSCI 4511W - Introduction to Artificial Intelligence
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Typically offered:
Every Fall & Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit.
prereq: 2041 or instr consent
CSCI 5511 - Artificial Intelligence I
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Prerequisites:
[2041 or #], grad student
Typically offered:
Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language.
prereq: [2041 or instr consent], grad student
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics.
prereq: 2031 or 2033 or instr consent
EE 4389W - Introduction to Predictive Learning
(WI)
Credits:
3.0
[max 3.0]
Typically offered:
Fall Odd Year
Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies.
prereq: [3025, ECE student] or STAT 3022; computer programming or MATLAB or similar environment is recommended for ECE students
EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.
A previous course in computer architecture is suggested but not required.
EE 5351 - Applied Parallel Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Parallel programming/architecture. Application development for many-core processors. Computational thinking, types of parallelism, programming models, mapping computations effectively to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, application case studies.
prereq: [4363 or equivalent], programming experience (C/C++ preferred)
EE 5364 - Advanced Computer Architecture
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5204/EE 5364
Typically offered:
Every Fall
Instruction set architecture, processor microarchitecture. Memory and I/O systems. Interactions between computer software and hardware. Methodologies of computer design.
prereq: [[4363 or CSci 4203], CSE grad student] or dept consent
EE 5371 - Computer Systems Performance Measurement and Evaluation
Credits:
3.0
[max 3.0]
Course Equivalencies:
EE 5371/5863
Typically offered:
Periodic Fall & Spring
Tools/techniques for analyzing computer hardware, software, system performance. Benchmark programs, measurement tools, performance metrics. Deterministic/probabilistic simulation techniques, random number generation/testing. Bottleneck analysis.
prereq: [4363 or 5361 or CSci 4203 or 5201], [CSE grad student] or dept consent
EE 5393 - Circuits, Computation, and Biology
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Connections between digital circuit design and synthetic/computational biology. Probabilistic, discrete-event simulation. Timing analysis. Information-Theoretic Analysis. Feedback in digital circuits/genetic regulatory systems. Synthesizing stochastic logic and probabilistic biochemistry.
CSCI 5204 - Advanced Computer Architecture
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5204/EE 5364
Typically offered:
Every Fall
Instruction set architecture, processor microarchitecture, memory, I/O systems. Interactions between computer software and hardware. Methodologies of computer design.
prereq: 4203 or EE 4363
EE 4231 - Linear Control Systems: Designed by Input/Output Methods
Credits:
3.0
[max 3.0]
Course Equivalencies:
AEM 4321/EE 4231
Typically offered:
Every Fall
Modeling, characteristics, performance of feedback control systems. Stability, root locus, frequency response methods. Digital implementation, hardware considerations.
prereq: [3015, [upper div CSE or grad student in CSE major]] or instr consent
EE 4233 - State Space Control System Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
State space models, performance evaluation, numerical issues for feedback control. Stability, state estimation, quadratic performance. Implementation, computational issues.
prereq: [3015, upper div CSE] or instr consent
EE 4341 - Embedded System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microcontroller interfacing for embedded system design. Exception handling/interrupts. Memory Interfacing. Parallel/serial input/output methods. System Buses and protocols. Serial Buses and component interfaces. Microcontroller Networks. Real-Time Operating Systems. Integral lab.
prereq: 2301, 2361, upper div CSE
EE 5271 - Robot Vision
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Modern visual perception for robotics that includes position and orientation, camera model and calibration, feature detection, multiple images, pose estimation, vision-based control, convolutional neural networks, reinforcement learning, deep Q-network, and visuomotor policy learning. [Math 2373 or equivalent; EE 1301 or equivalent basic programming course]
EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.
A previous course in computer architecture is suggested but not required.
CSCI 4511W - Introduction to Artificial Intelligence
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Typically offered:
Every Fall & Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit.
prereq: 2041 or instr consent
CSCI 5143 - Real-Time and Embedded Systems
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Periodic Spring
Real-time systems that require timely response by computer to external stimulus. Embedded systems in which computer is part of machine. Increasing importance of these systems in commercial products. How to control robots and video game consoles. Lecture, informal lab.
prereq: [4061 or instr consent], experience with C language
CSCI 5511 - Artificial Intelligence I
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Prerequisites:
[2041 or #], grad student
Typically offered:
Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language.
prereq: [2041 or instr consent], grad student
CSCI 5512 - Artificial Intelligence II
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5512W/CSci 5512
Typically offered:
Every Spring
Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods.
prereq: [STAT 3021, 4041] or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence.
Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5525 - Machine Learning: Analysis and Methods
Credits:
3.0
[max 3.0]
Typically offered:
Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models.
prereq: Grad student or instr consent
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics.
prereq: 2031 or 2033 or instr consent
CSCI 5552 - Sensing and Estimation in Robotics
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping.
prereq: [5551, Stat 3021] or instr consent
CSCI 5561 - Computer Vision
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications.
prereq: CSci 5511, 5521, or instructor consent.
EE 4301 - Digital Design With Programmable Logic
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Summer
Introduction to system design/simulation. Design using Verilog code/synthesis. Emulation using Verilog code.
prereq: 2301, [1301 or CSCI 1113 or CSCI 1901]
EE 5301 - VLSI Design Automation I
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Basic graph/numerical algorithms. Algorithms for logic/high-level synthesis. Simulation algorithms at logic/circuit level. Physical-design algorithms.
prereq: [2301, CSE grad student] or dept consent
EE 5302 - VLSI Design Automation II
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Basic algorithms, computational complexity. High-level synthesis. Test generation. Power estimation. Timing optimization. Current topics.
prereq: [5301, CSE grad student] or dept consent
EE 5323 - VLSI Design I
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Combinational static CMOS circuits. Transmission gate networks. Clocking strategies, sequential circuits. CMOS process flows, design rules, structured layout techniques. Dynamic circuits, including Domino CMOS and DCVS. Performance analysis, design optimization, device sizing.
prereq: [2301, 3115, CSE grad student] or dept consent
EE 5324 - VLSI Design II
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
CMOS arithmetic logic units, high-speed carry chains, fast CMOS multipliers. High-speed performance parallel shifters. CMOS memory cells, array structures, read/write circuits. Design for testability, including scan design and built-in self test. VLSI case studies.
prereq: [5323, CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation.
prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5329 - VLSI Digital Signal Processing Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Programmable architectures for signal/media processing. Data-flow representation. Architecture transformations. Low-power design. Architectures for two's complement/redundant representation, carry-save, and canonic signed digit. Scheduling/allocation for high-level synthesis.
prereq: [[5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5333 - Analog Integrated Circuit Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Fundamental circuits for analog signal processing. Design issues associated with MOS/BJT devices. Design/testing of circuits. Selected topics (e.g., modeling of basic IC components, design of operational amplifier or comparator or analog sampled-data circuit filter).
prereq: [3115, CSE grad student] or dept consent
EE 4501 - Communications Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Systems for transmission/reception of digital/analog information. Characteristics/design of wired/wireless communication systems. Baseband, digital, and carrier-based techniques. Modulation. Coding. Electronic noise and its effects on design/performance.
prereq: 3025
CSCI 4131 - Internet Programming
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4131/CSci 5131
Typically offered:
Every Fall & Spring
Issues in internet programming. Internet history, architecture/protocols, network programming, Web architecture. Client-server architectures and protocols. Client-side programming, server-side programming, dynamic HTML, Java programming, object-oriented architecture/design, distributed object computing, Web applications.
prereq: 4061, 4211 recommended, cannot be taken for grad CSci cr
CSCI 4211 - Introduction to Computer Networks
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4211/CSci 5211/INET 4002
Typically offered:
Every Fall & Spring
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, routing, transport, network programming interfaces, networked applications. Examples from Ethernet, Token Ring, TCP/IP, HTTP, WWW.
prereq: 4061 or instr consent; basic knowledge of [computer architecture, operating systems] recommended, cannot be taken for grad CSci cr
CSCI 5211 - Data Communications and Computer Networks
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4211/CSci 5211/INET 4002
Typically offered:
Every Fall
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, network layer/routing protocols, transport, congestion/flow control, emerging high-speed networks, network programming interfaces, networked applications. Case studies using Ethernet, Token Ring, FDDI, TCP/IP, ATM, Email, HTTP, and WWW.
prereq: [4061 or instr consent], basic knowledge of [computer architecture, operating systems, probability], grad student
CSCI 5221 - Foundations of Advanced Networking
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Design principles, protocol mechanisms. Network algorithmics, implementation techniques. Advanced network architectures, state-of-art/emerging networking technologies/applications, network modeling. Simulation, experiments.
prereq: 4211 or 5211 or equiv; intro course in computer networks recommended
CSCI 5271 - Introduction to Computer Security
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Concepts of computer, network, and information security.
Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls.
prereq: 4061 or 5103 or equiv or instr consent
CSCI 5471 - Modern Cryptography
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Introduction to cryptography. Theoretical foundations, practical applications. Threats, attacks, and countermeasures, including cryptosystems and cryptographic protocols. Secure systems/networks. History of cryptography, encryption (conventional, public key), digital signatures, hash functions, message authentication codes, identification, authentication, applications.
prereq: [2011, 4041, [familiarity with number theory or finite fields]] or instr consent
EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
Algorithm techniques for enhancing the scalability of parallel software: scatter-to-gather, problem decomposition, binning, privatization, tiling, regularization, compaction, double-buffering, and data layout. These techniques address the most challenging problems in building scalable parallel software: limited parallelism, data contention, insufficient memory bandwidth, load balance, and communication latency. Programming assignments will be given to reinforce the understanding of the techniques.
prereq: basic knowledge of CUDA, experience working in a Unix environment, and experience developing and running scientific codes written in C or C++. Completion of EE 5351 is not required but highly recommended.
CSCI 4011 - Formal Languages and Automata Theory
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Logical/mathematical foundations of computer science. Formal languages, their correspondence to machine models. Lexical analysis, string matching, parsing. Decidability, undecidability, limits of computability. Computational complexity.
prereq: 2041 or instr consent
CSCI 4707 - Practice of Database Systems
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4707/CSci 5707/INET 4707
Typically offered:
Every Fall & Spring
Concepts, conceptual data models, case studies, common data manipulation languages, logical data models, database design, facilities for database security/integrity, applications.
prereq: 4041 or instr consent
CSCI 5103 - Operating Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Conceptual foundation of operating system designs and implementations. Relationships between operating system structures and machine architectures. UNIX implementation mechanisms as examples.
prereq: 4061 or instr consent
CSCI 5105 - Introduction to Distributed Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Distributed system design and implementation. Distributed communication and synchronization, data replication and consistency, distributed file systems, fault tolerance, and distributed scheduling.
prereq: [5103 or equiv] or instr consent
CSCI 5106 - Programming Languages
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages.
prereq: 4011 or instr consent
CSCI 5117 - Developing the Interactive Web
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Hands-on design experience using modern web development tools. Students work in teams to develop software programs using each of four toolkits. Analyze developments in forum posts and classroom discussions.
prereq: 4131 or 5131 or instr consent; upper div or grad in CSci recommended
CSCI 5125 - Collaborative and Social Computing
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Introduction to computer-supported cooperative work, social computing. Technology, research methods, theory, case studies of group computing systems. Readings, hands-on experience.
prereq: 5115 or instr consent
CSCI 5161 - Introduction to Compilers
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Techniques for translating modern programming languages to intermediate forms or machine-executable instructions/their organization into compiler. Lexical analysis, syntax analysis, semantic analysis, data flow analysis, code generation. Compiler project for prototypical language.
prereq: [2021, 5106] or instr consent
CSCI 5271 - Introduction to Computer Security
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Concepts of computer, network, and information security.
Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls.
prereq: 4061 or 5103 or equiv or instr consent
CSCI 5421 - Advanced Algorithms and Data Structures
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains.
prereq: 4041 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.
prereq: 4041 or instr consent
CSCI 5707 - Principles of Database Systems
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4707/CSci 5707/INET 4707
Typically offered:
Every Fall
Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends.
prereq: [4041 or instr consent], grad student
CSCI 5708 - Architecture and Implementation of Database Management Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules.
prereq: 4041 or 4707 or 5707 or instr. consent
CSCI 5801 - Software Engineering I
Credits:
3.0
[max 3.0]
Prerequisites:
2041 or #
Typically offered:
Every Fall
Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance.
prereq: 2041 or instr consent
CSCI 5802 - Software Engineering II
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Introduction to software testing, software maturity models, cost specification models, bug estimation, software reliability models, software complexity, quality control, and experience report. Student groups specify, design, implement, and test partial software systems. Application of general software development methods and principles from 5801.
prereq: 5801 or instr consent
EE 4521 - Introduction to Machine Learning and Data Science for Electrical and Computer Engineers
Credits:
3.0
[max 3.0]
Course Equivalencies:
EE 4521/EE 5521
Typically offered:
Every Fall
Computational techniques for analysis and inference from data. Python language programming. Elementary numerical optimization and statistical data analysis. Computational methods for clustering, dimensionality reduction, classification, regression, and time series analysis. Construction, training, and utilization of deep neural networks. Application case studies using datasets arising in Electrical and Computer Engineering.
prereq: EE 3025; Math 2263 or 2374; Math 2142, 2243, 2373 or CSci 2033
CSCI 5302 - Analysis of Numerical Algorithms
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Additional topics in numerical analysis. Interpolation, approximation, extrapolation, numerical integration/differentiation, numerical solutions of ordinary differential equations. Introduction to optimization techniques.
prereq: 2031 or 2033 or instr consent
CSCI 5304 - Computational Aspects of Matrix Theory
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Perturbation theory for linear systems and eigenvalue problems. Direct/iterative solution of large linear systems. Matrix factorizations. Computation of eigenvalues/eigenvectors. Singular value decomposition. LAPACK/other software packages. Introduction to sparse matrix methods.
prereq: 2031 or 2033 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.
prereq: 4041 or instr consent
CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Computational methods for analyzing, integrating, and
deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets.
prereq: 3003 or 4041 or instr consent
CSCI 5481 - Computational Techniques for Genomics
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks.
prereq: 4041 or instr consent
CSCI 5523 - Introduction to Data Mining
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects.
prereq: 4041 or equiv or instr consent
CSCI 5609 - Visualization
Credits:
3.0
[max 3.0]
Typically offered:
Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization.
prereq: [1913, 4041] or equiv or instr consent
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc.
prereq: Familiarity with Java, C++, or Python
CSCI 4611 - Programming Interactive Computer Graphics and Games
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Tools/techniques for programming games/interactive computer graphics. Event loops, rendering/animation, polygonal models, texturing, physical simulation. Modern graphics toolkits. History/future of computer games technology. Social impact of interactive computer graphics.
prereq: 2021 or instr consent
CSCI 5115 - User Interface Design, Implementation and Evaluation
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project.
prereq: 4041 or instr consent
In this course, you will learn and apply the principles of embodied computing to human-centered challenges. Through a semester-long team project, you will learn and demonstrate mastery of human-centered embodied computing through two phases: (1) investigating human needs and current embodied practices and (2) rapidly prototyping and iterating embodied computing solutions. One of the ways you will demonstrate this mastery is through the collaborative creation of a written document and project capstone video describing your process and prototype.
prereq: CSci 4041, upper division or graduate student, or instructor permission; CSci 5115 or equivalent recommended.
CSCI 5607 - Fundamentals of Computer Graphics 1
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Fundamental algorithms in computer graphics. Emphasizes programming projects in C/C++. Scan conversion, hidden surface removal, geometrical transformations, projection, illumination/shading, parametric cubic curves, texture mapping, antialising, ray tracing. Developing graphics software, graphics research.
prereq: concurrent registration is required (or allowed) in 2033, concurrent registration is required (or allowed) in 3081
CSCI 5611 - Animation & Planning in Games
Credits:
3.0
[max 3.0]
Typically offered:
Fall Odd Year
Theory behind algorithms used to bring virtual worlds to life. Computer animation topics. Real-time, interactive techniques used in modern games. Physically-based animation, motion planning, character animation, simulation in virtual worlds.
prereq: 4041 or 4611 or instr consent
CSCI 5619 - Virtual Reality and 3D Interaction
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
Introduction to software, technology/applications in virtual/augmented reality, 3D user interaction. Overview of current research. Hands-on projects.
prereq: 4611 or 5607 or 5115 or equiv or instr consent
DES 2701 - Creative Design Methods
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Every Fall
This class is an introduction to a variety of tools and methods used in developing new products, services, and experiences. The focus of the class is on the early stages of design which includes user research, market research, idea generation methods, concept evaluation, concept selection, intellectual property, and idea presentation. Students will learn the divergent and convergent design thinking process to frame problems, and generate, refine, and communicate ideas. Students work individually and in groups applying the content taught in lecture to multiple assignments and a semester-long design project.
PDES 2702 - Concept Sketching
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Every Fall
This class is an introduction to manual sketching techniques, specifically for the communication of conceptual product ideas. The focus of this class is on free-hand perspective drawing. Students begin with basic principles, simple shapes, light and shadow, and later learn how to combine forms to create conceptual objects with realistic perspective. In this class, there are weekly drawing assignments and presentations.
PDES 3711 - Product Innovation Lab
Credits:
4.0
[max 4.0]
Course Equivalencies:
PDes 3711/PDes 5711
Grading Basis:
A-F only
Typically offered:
Every Spring
A hands-on experience in integrated product design and development processes. Elements of industrial design, engineering, business, and humanities are applied to a semester-long product design project. Cross-functional teams of students in different majors work together to design and develop new consumer product concepts with guidance from a community of industry mentors.
prereq: PDes 2772 OR Junior/Senior (any major) or permission from instructor
PDES 5711 - Product Innovation Lab
Credits:
4.0
[max 4.0]
Course Equivalencies:
PDes 3711/PDes 5711
Grading Basis:
A-F only
Typically offered:
Every Spring
A hands-on experience in integrated product design and development processes. Elements of industrial design, engineering, business, and humanities are applied to a semester-long product design project. Cross-functional teams of students in different majors work together to design and develop new consumer product concepts with guidance from a community of industry mentors.
EE 4951W - Senior Design Project
(WI)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Team participation in formulating/solving open-ended design problems. Oral/written presentations.
prereq: 3015, 3115, 3102, attendance first day of class
EE 4111 - Advanced Analog Electronics Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Basic integrated circuit building blocks of differential amplifiers, high bandwidth, instrumentation amplifiers. Current/voltage references. Feedback, stability, and noise in electronic circuits. Integral lab.
prereq: 3015, 3115
EE 4163 - Energy Conversion and Storage Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Provides laboratory experiences with the topics of 4161W, including the fundamental physics and chemistry of selected energy conversion and energy storage devices, their application, and their connection strategies in electric power applications.
prereq: concurrent registration is required (or allowed) in 4161W
EE 4235 - Linear Control Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Lab to accompany 4231.
prereq: 4231 or concurrent registration is required (or allowed) in 4231
EE 4237 - State Space Control Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Lab to accompany 4233.
prereq: 4233 or concurrent registration is required (or allowed) in 4233; no cr for [EE or CompE] grad students
EE 4301 - Digital Design With Programmable Logic
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Summer
Introduction to system design/simulation. Design using Verilog code/synthesis. Emulation using Verilog code.
prereq: 2301, [1301 or CSCI 1113 or CSCI 1901]
EE 4341 - Embedded System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microcontroller interfacing for embedded system design. Exception handling/interrupts. Memory Interfacing. Parallel/serial input/output methods. System Buses and protocols. Serial Buses and component interfaces. Microcontroller Networks. Real-Time Operating Systems. Integral lab.
prereq: 2301, 2361, upper div CSE
EE 4505 - Communications Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Experiments in analysis/design of wired/wireless communication systems. Lab to accompany 4501.
prereq: 4501 or concurrent registration is required (or allowed) in 4501
EE 4703 - Electric Drives Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Laboratory to accompany 4701. Simulink-based simulations of electric machines/drives in applications such as energy conservation and motion control in robotics.
prereq: 4701 or concurrent registration is required (or allowed) in 4701
EE 4722 - Power System Analysis Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Lab analysis of AC power systems, power system networks, power flow, short circuit, transient stability.
prereq: 4721 or concurrent registration is required (or allowed) in 4721
EE 4743 - Switch-Mode Power Electronics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Laboratory to accompany 4741. PSpice-/Simulink-based simulations of converters, topologies, and control in switch-mode dc power supplies, motor drives for motion control, and inverters for interfacing renewable energy sources to utility grid.
prereq: 4741 or concurrent registration is required (or allowed) in 4741
EE 4930 - Special Topics in Electrical and Computer Engineering Laboratory
Credits:
1.0
-2.0
[max 6.0]
Grading Basis:
A-F only
Typically offered:
Periodic Fall, Spring & Summer
Lab work not available in regular courses. Topics vary.
prereq: CSE sr or grad student or instr consent
EE 5141 - Introduction to Microsystem Technology
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microelectromechanical systems composed of microsensors, microactuators, and electronics integrated onto common substrate. Design, fabrication, and operation principles. Labs on micromachining, photolithography, etching, thin film deposition, metallization, packaging, and device characterization.
prereq: [3161, 3601, CSE grad student] or dept consent
EE 5173 - Basic Microelectronics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Students fabricate a polysilicon gate, single-layer metal, NMOS chip, performing 80 percent of processing, including photolithography, diffusion, oxidation, and etching. In-process measurement results are compared with final electrical test results. Simple circuits are used to estimate technology performance.
prereq: [[5171 or concurrent registration is required (or allowed) in 5171], CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation.
prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5373 - Data Modeling Using R
Credits:
1.0
[max 1.0]
Grading Basis:
A-F only
Typically offered:
Periodic Fall & Spring
Introduction to data modeling and the R language programming. Multi-factor linear regression modeling.
Residual analysis and model quality evaluation. Response prediction. Training and testing. Integral lab.
An introductory course in probability and statistics is suggested but not required; basic programming skills in some high-level programming language, such as C/C++, Java, Fortran, etc also suggested.
EE 5545 - Digital Signal Processing Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Real-time implementation of digital signal processing (DSP) algorithms, including filtering, sample-rate conversion, and FFT-based spectral analysis. Implementation on a modern DSP Platform. Processor architecture. Arithmetic operations. Real-time processing issues. Processor limitations. Integral laboratory.
prereq: [4541, CSE grad student] or dept consent
EE 5613 - RF/Microwave Circuit Design Laboratory
Credits:
2.0
[max 2.0]
Grading Basis:
A-F only
Typically offered:
Every Spring
Scattering parameters, planar lumped circuits, transmission lines, RF/microwave substrate materials, matching networks/tuning elements, resonators, filters, combiners/dividers, couplers. Integral lab.
prereq: [[5601 or concurrent registration is required (or allowed) in 5601], CSE grad student] or dept consent
EE 5622 - Physical Optics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Fundamental optical techniques. Diffraction and optical pattern recognition. Spatial/temporal coherence. Interferometry. Speckle. Coherent/incoherent imaging. Coherent image processing. Fiber Optics.
prereq: [[5621 or concurrent registration is required (or allowed) in 5621], CSE grad student] or dept consent
EE 5657 - Physical Principles of Thin Film Technology
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Fabrication, characterization, and application of thin film and nanostructured materials and devices. Focuses on vacuum deposition. Materials science. Hands-on, team-based labs.
EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Periodic Spring
Lab to accompany 5705.
prereq: 5705 or concurrent registration is required (or allowed) in 5705
EE 5811 - Biological Instrumentation
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
This course will cover the physics and technology of biological instruments. The operating principles of optical, electrical, and mechanical biosensors will be discussed, followed by transport and delivery of biomolecules to the sensors. Techniques to manufacture these sensing devices, along with microfluidic packaging, will be covered. Lectures will be complemented by lab demo sessions to give students hands-on experiences in microfluidic chip fabrication, microscopy, and particle trapping experiments.
CSCI 4511W - Introduction to Artificial Intelligence
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Typically offered:
Every Fall & Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit.
prereq: 2041 or instr consent
CSCI 5511 - Artificial Intelligence I
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Prerequisites:
[2041 or #], grad student
Typically offered:
Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language.
prereq: [2041 or instr consent], grad student
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics.
prereq: 2031 or 2033 or instr consent
EE 4389W - Introduction to Predictive Learning
(WI)
Credits:
3.0
[max 3.0]
Typically offered:
Fall Odd Year
Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies.
prereq: [3025, ECE student] or STAT 3022; computer programming or MATLAB or similar environment is recommended for ECE students
EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.
A previous course in computer architecture is suggested but not required.
EE 5351 - Applied Parallel Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Parallel programming/architecture. Application development for many-core processors. Computational thinking, types of parallelism, programming models, mapping computations effectively to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, application case studies.
prereq: [4363 or equivalent], programming experience (C/C++ preferred)
EE 5364 - Advanced Computer Architecture
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5204/EE 5364
Typically offered:
Every Fall
Instruction set architecture, processor microarchitecture. Memory and I/O systems. Interactions between computer software and hardware. Methodologies of computer design.
prereq: [[4363 or CSci 4203], CSE grad student] or dept consent
EE 5371 - Computer Systems Performance Measurement and Evaluation
Credits:
3.0
[max 3.0]
Course Equivalencies:
EE 5371/5863
Typically offered:
Periodic Fall & Spring
Tools/techniques for analyzing computer hardware, software, system performance. Benchmark programs, measurement tools, performance metrics. Deterministic/probabilistic simulation techniques, random number generation/testing. Bottleneck analysis.
prereq: [4363 or 5361 or CSci 4203 or 5201], [CSE grad student] or dept consent
EE 5393 - Circuits, Computation, and Biology
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Connections between digital circuit design and synthetic/computational biology. Probabilistic, discrete-event simulation. Timing analysis. Information-Theoretic Analysis. Feedback in digital circuits/genetic regulatory systems. Synthesizing stochastic logic and probabilistic biochemistry.
CSCI 5204 - Advanced Computer Architecture
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5204/EE 5364
Typically offered:
Every Fall
Instruction set architecture, processor microarchitecture, memory, I/O systems. Interactions between computer software and hardware. Methodologies of computer design.
prereq: 4203 or EE 4363
EE 4231 - Linear Control Systems: Designed by Input/Output Methods
Credits:
3.0
[max 3.0]
Course Equivalencies:
AEM 4321/EE 4231
Typically offered:
Every Fall
Modeling, characteristics, performance of feedback control systems. Stability, root locus, frequency response methods. Digital implementation, hardware considerations.
prereq: [3015, [upper div CSE or grad student in CSE major]] or instr consent
EE 4233 - State Space Control System Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
State space models, performance evaluation, numerical issues for feedback control. Stability, state estimation, quadratic performance. Implementation, computational issues.
prereq: [3015, upper div CSE] or instr consent
EE 4341 - Embedded System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microcontroller interfacing for embedded system design. Exception handling/interrupts. Memory Interfacing. Parallel/serial input/output methods. System Buses and protocols. Serial Buses and component interfaces. Microcontroller Networks. Real-Time Operating Systems. Integral lab.
prereq: 2301, 2361, upper div CSE
EE 5271 - Robot Vision
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Modern visual perception for robotics that includes position and orientation, camera model and calibration, feature detection, multiple images, pose estimation, vision-based control, convolutional neural networks, reinforcement learning, deep Q-network, and visuomotor policy learning. [Math 2373 or equivalent; EE 1301 or equivalent basic programming course]
EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.
A previous course in computer architecture is suggested but not required.
CSCI 4511W - Introduction to Artificial Intelligence
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Typically offered:
Every Fall & Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit.
prereq: 2041 or instr consent
CSCI 5143 - Real-Time and Embedded Systems
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Periodic Spring
Real-time systems that require timely response by computer to external stimulus. Embedded systems in which computer is part of machine. Increasing importance of these systems in commercial products. How to control robots and video game consoles. Lecture, informal lab.
prereq: [4061 or instr consent], experience with C language
CSCI 5511 - Artificial Intelligence I
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Prerequisites:
[2041 or #], grad student
Typically offered:
Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language.
prereq: [2041 or instr consent], grad student
CSCI 5512 - Artificial Intelligence II
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5512W/CSci 5512
Typically offered:
Every Spring
Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods.
prereq: [STAT 3021, 4041] or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence.
Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5525 - Machine Learning: Analysis and Methods
Credits:
3.0
[max 3.0]
Typically offered:
Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models.
prereq: Grad student or instr consent
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics.
prereq: 2031 or 2033 or instr consent
CSCI 5552 - Sensing and Estimation in Robotics
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping.
prereq: [5551, Stat 3021] or instr consent
CSCI 5561 - Computer Vision
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications.
prereq: CSci 5511, 5521, or instructor consent.
EE 4301 - Digital Design With Programmable Logic
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Summer
Introduction to system design/simulation. Design using Verilog code/synthesis. Emulation using Verilog code.
prereq: 2301, [1301 or CSCI 1113 or CSCI 1901]
EE 5301 - VLSI Design Automation I
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Basic graph/numerical algorithms. Algorithms for logic/high-level synthesis. Simulation algorithms at logic/circuit level. Physical-design algorithms.
prereq: [2301, CSE grad student] or dept consent
EE 5302 - VLSI Design Automation II
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Basic algorithms, computational complexity. High-level synthesis. Test generation. Power estimation. Timing optimization. Current topics.
prereq: [5301, CSE grad student] or dept consent
EE 5323 - VLSI Design I
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Combinational static CMOS circuits. Transmission gate networks. Clocking strategies, sequential circuits. CMOS process flows, design rules, structured layout techniques. Dynamic circuits, including Domino CMOS and DCVS. Performance analysis, design optimization, device sizing.
prereq: [2301, 3115, CSE grad student] or dept consent
EE 5324 - VLSI Design II
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
CMOS arithmetic logic units, high-speed carry chains, fast CMOS multipliers. High-speed performance parallel shifters. CMOS memory cells, array structures, read/write circuits. Design for testability, including scan design and built-in self test. VLSI case studies.
prereq: [5323, CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation.
prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5329 - VLSI Digital Signal Processing Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Programmable architectures for signal/media processing. Data-flow representation. Architecture transformations. Low-power design. Architectures for two's complement/redundant representation, carry-save, and canonic signed digit. Scheduling/allocation for high-level synthesis.
prereq: [[5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5333 - Analog Integrated Circuit Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Fundamental circuits for analog signal processing. Design issues associated with MOS/BJT devices. Design/testing of circuits. Selected topics (e.g., modeling of basic IC components, design of operational amplifier or comparator or analog sampled-data circuit filter).
prereq: [3115, CSE grad student] or dept consent
EE 4501 - Communications Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Systems for transmission/reception of digital/analog information. Characteristics/design of wired/wireless communication systems. Baseband, digital, and carrier-based techniques. Modulation. Coding. Electronic noise and its effects on design/performance.
prereq: 3025
CSCI 4131 - Internet Programming
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4131/CSci 5131
Typically offered:
Every Fall & Spring
Issues in internet programming. Internet history, architecture/protocols, network programming, Web architecture. Client-server architectures and protocols. Client-side programming, server-side programming, dynamic HTML, Java programming, object-oriented architecture/design, distributed object computing, Web applications.
prereq: 4061, 4211 recommended, cannot be taken for grad CSci cr
CSCI 4211 - Introduction to Computer Networks
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4211/CSci 5211/INET 4002
Typically offered:
Every Fall & Spring
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, routing, transport, network programming interfaces, networked applications. Examples from Ethernet, Token Ring, TCP/IP, HTTP, WWW.
prereq: 4061 or instr consent; basic knowledge of [computer architecture, operating systems] recommended, cannot be taken for grad CSci cr
CSCI 5211 - Data Communications and Computer Networks
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4211/CSci 5211/INET 4002
Typically offered:
Every Fall
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, network layer/routing protocols, transport, congestion/flow control, emerging high-speed networks, network programming interfaces, networked applications. Case studies using Ethernet, Token Ring, FDDI, TCP/IP, ATM, Email, HTTP, and WWW.
prereq: [4061 or instr consent], basic knowledge of [computer architecture, operating systems, probability], grad student
CSCI 5221 - Foundations of Advanced Networking
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Design principles, protocol mechanisms. Network algorithmics, implementation techniques. Advanced network architectures, state-of-art/emerging networking technologies/applications, network modeling. Simulation, experiments.
prereq: 4211 or 5211 or equiv; intro course in computer networks recommended
CSCI 5271 - Introduction to Computer Security
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Concepts of computer, network, and information security.
Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls.
prereq: 4061 or 5103 or equiv or instr consent
CSCI 5471 - Modern Cryptography
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Introduction to cryptography. Theoretical foundations, practical applications. Threats, attacks, and countermeasures, including cryptosystems and cryptographic protocols. Secure systems/networks. History of cryptography, encryption (conventional, public key), digital signatures, hash functions, message authentication codes, identification, authentication, applications.
prereq: [2011, 4041, [familiarity with number theory or finite fields]] or instr consent
EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
Algorithm techniques for enhancing the scalability of parallel software: scatter-to-gather, problem decomposition, binning, privatization, tiling, regularization, compaction, double-buffering, and data layout. These techniques address the most challenging problems in building scalable parallel software: limited parallelism, data contention, insufficient memory bandwidth, load balance, and communication latency. Programming assignments will be given to reinforce the understanding of the techniques.
prereq: basic knowledge of CUDA, experience working in a Unix environment, and experience developing and running scientific codes written in C or C++. Completion of EE 5351 is not required but highly recommended.
CSCI 4011 - Formal Languages and Automata Theory
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Logical/mathematical foundations of computer science. Formal languages, their correspondence to machine models. Lexical analysis, string matching, parsing. Decidability, undecidability, limits of computability. Computational complexity.
prereq: 2041 or instr consent
CSCI 4707 - Practice of Database Systems
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4707/CSci 5707/INET 4707
Typically offered:
Every Fall & Spring
Concepts, conceptual data models, case studies, common data manipulation languages, logical data models, database design, facilities for database security/integrity, applications.
prereq: 4041 or instr consent
CSCI 5103 - Operating Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Conceptual foundation of operating system designs and implementations. Relationships between operating system structures and machine architectures. UNIX implementation mechanisms as examples.
prereq: 4061 or instr consent
CSCI 5105 - Introduction to Distributed Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Distributed system design and implementation. Distributed communication and synchronization, data replication and consistency, distributed file systems, fault tolerance, and distributed scheduling.
prereq: [5103 or equiv] or instr consent
CSCI 5106 - Programming Languages
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages.
prereq: 4011 or instr consent
CSCI 5117 - Developing the Interactive Web
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Hands-on design experience using modern web development tools. Students work in teams to develop software programs using each of four toolkits. Analyze developments in forum posts and classroom discussions.
prereq: 4131 or 5131 or instr consent; upper div or grad in CSci recommended
CSCI 5125 - Collaborative and Social Computing
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Introduction to computer-supported cooperative work, social computing. Technology, research methods, theory, case studies of group computing systems. Readings, hands-on experience.
prereq: 5115 or instr consent
CSCI 5161 - Introduction to Compilers
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Techniques for translating modern programming languages to intermediate forms or machine-executable instructions/their organization into compiler. Lexical analysis, syntax analysis, semantic analysis, data flow analysis, code generation. Compiler project for prototypical language.
prereq: [2021, 5106] or instr consent
CSCI 5271 - Introduction to Computer Security
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Concepts of computer, network, and information security.
Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls.
prereq: 4061 or 5103 or equiv or instr consent
CSCI 5421 - Advanced Algorithms and Data Structures
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains.
prereq: 4041 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.
prereq: 4041 or instr consent
CSCI 5707 - Principles of Database Systems
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4707/CSci 5707/INET 4707
Typically offered:
Every Fall
Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends.
prereq: [4041 or instr consent], grad student
CSCI 5708 - Architecture and Implementation of Database Management Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules.
prereq: 4041 or 4707 or 5707 or instr. consent
CSCI 5801 - Software Engineering I
Credits:
3.0
[max 3.0]
Prerequisites:
2041 or #
Typically offered:
Every Fall
Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance.
prereq: 2041 or instr consent
CSCI 5802 - Software Engineering II
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Introduction to software testing, software maturity models, cost specification models, bug estimation, software reliability models, software complexity, quality control, and experience report. Student groups specify, design, implement, and test partial software systems. Application of general software development methods and principles from 5801.
prereq: 5801 or instr consent
EE 4521 - Introduction to Machine Learning and Data Science for Electrical and Computer Engineers
Credits:
3.0
[max 3.0]
Course Equivalencies:
EE 4521/EE 5521
Typically offered:
Every Fall
Computational techniques for analysis and inference from data. Python language programming. Elementary numerical optimization and statistical data analysis. Computational methods for clustering, dimensionality reduction, classification, regression, and time series analysis. Construction, training, and utilization of deep neural networks. Application case studies using datasets arising in Electrical and Computer Engineering.
prereq: EE 3025; Math 2263 or 2374; Math 2142, 2243, 2373 or CSci 2033
CSCI 5302 - Analysis of Numerical Algorithms
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Additional topics in numerical analysis. Interpolation, approximation, extrapolation, numerical integration/differentiation, numerical solutions of ordinary differential equations. Introduction to optimization techniques.
prereq: 2031 or 2033 or instr consent
CSCI 5304 - Computational Aspects of Matrix Theory
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Perturbation theory for linear systems and eigenvalue problems. Direct/iterative solution of large linear systems. Matrix factorizations. Computation of eigenvalues/eigenvectors. Singular value decomposition. LAPACK/other software packages. Introduction to sparse matrix methods.
prereq: 2031 or 2033 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.
prereq: 4041 or instr consent
CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Computational methods for analyzing, integrating, and
deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets.
prereq: 3003 or 4041 or instr consent
CSCI 5481 - Computational Techniques for Genomics
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks.
prereq: 4041 or instr consent
CSCI 5523 - Introduction to Data Mining
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects.
prereq: 4041 or equiv or instr consent
CSCI 5609 - Visualization
Credits:
3.0
[max 3.0]
Typically offered:
Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization.
prereq: [1913, 4041] or equiv or instr consent
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc.
prereq: Familiarity with Java, C++, or Python
CSCI 4611 - Programming Interactive Computer Graphics and Games
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Tools/techniques for programming games/interactive computer graphics. Event loops, rendering/animation, polygonal models, texturing, physical simulation. Modern graphics toolkits. History/future of computer games technology. Social impact of interactive computer graphics.
prereq: 2021 or instr consent
CSCI 5115 - User Interface Design, Implementation and Evaluation
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project.
prereq: 4041 or instr consent
In this course, you will learn and apply the principles of embodied computing to human-centered challenges. Through a semester-long team project, you will learn and demonstrate mastery of human-centered embodied computing through two phases: (1) investigating human needs and current embodied practices and (2) rapidly prototyping and iterating embodied computing solutions. One of the ways you will demonstrate this mastery is through the collaborative creation of a written document and project capstone video describing your process and prototype.
prereq: CSci 4041, upper division or graduate student, or instructor permission; CSci 5115 or equivalent recommended.
CSCI 5607 - Fundamentals of Computer Graphics 1
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Fundamental algorithms in computer graphics. Emphasizes programming projects in C/C++. Scan conversion, hidden surface removal, geometrical transformations, projection, illumination/shading, parametric cubic curves, texture mapping, antialising, ray tracing. Developing graphics software, graphics research.
prereq: concurrent registration is required (or allowed) in 2033, concurrent registration is required (or allowed) in 3081
CSCI 5611 - Animation & Planning in Games
Credits:
3.0
[max 3.0]
Typically offered:
Fall Odd Year
Theory behind algorithms used to bring virtual worlds to life. Computer animation topics. Real-time, interactive techniques used in modern games. Physically-based animation, motion planning, character animation, simulation in virtual worlds.
prereq: 4041 or 4611 or instr consent
CSCI 5619 - Virtual Reality and 3D Interaction
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
Introduction to software, technology/applications in virtual/augmented reality, 3D user interaction. Overview of current research. Hands-on projects.
prereq: 4611 or 5607 or 5115 or equiv or instr consent
DES 1000 - D@MN: Design@Minnesota
(AH)
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Every Fall & Spring
In DES 1000, students learn to use an iterative design process to define real-world challenges, and propose innovative solutions for social impact. Building soft-skills such as collaboration, visual and verbal communication, and empathy is a critical outcome of the course.
DES 1101V - Honors: Introduction to Design Thinking
(AH, WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Des 1101W/Des 1101V
Grading Basis:
A-F only
Typically offered:
Every Fall
Theories/processes that underpin design thinking. Interactions between humans and their natural, social, and designed environments where purposeful design helps determine quality of interaction. Design professions.
prereq: Honors student
DES 1101W - Introduction to Design Thinking
(AH, WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
Des 1101W/Des 1101V
Grading Basis:
A-F only
Typically offered:
Every Fall & Spring
Theories/processes that underpin design thinking. Interactions between humans and their natural, social, and designed environments where purposeful design helps determine quality of interaction. Design professions.
LA 1401 - The Designed Environment
(AH)
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Examination of relationships between place and space, and realms of the ideal and real, public and private. Survey of how the fields of architecture, landscape architecture, and urban design have explored those issues.
LA 1601 - Design and Equity
(DSJ, AH)
Credits:
3.0
[max 3.0]
Course Equivalencies:
LA 1601/LA 3601
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Investigate world from new perspectives. Spaces of everyday life that reflect/shape values. Meets with LA 3601.
LA 3601 - Design and Equity
(DSJ, AH)
Credits:
3.0
[max 3.0]
Course Equivalencies:
LA 1601/LA 3601
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Investigate world from new perspectives. Spaces of everyday life that reflect/shape values. Meets with LA 1601.
ARCH 5611 - Design in the Digital Age
Credits:
3.0
[max 3.0]
Course Equivalencies:
Arch 3611/Arch 5611
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Introduction to design, design process. Developing/understanding ways of seeing, thinking, and acting as a designer. Changes in design being wrought by digital technology. Team design project.
prereq: Grad student or upper level undergrad student
DES 3131 - User Experience in Design
Credits:
4.0
[max 4.0]
Grading Basis:
A-F only
Typically offered:
Every Fall
Introduction to theories/principles of human interaction with designed objects. Focuses on affect/emotional quality of designs. Objects, interfaces, environments. Digitally mediated experiences.
GDES 5341 - Interaction Design
Credits:
3.0
[max 3.0]
Course Equivalencies:
DHA 4384/GDES 5341
Grading Basis:
A-F or Aud
Typically offered:
Every Fall & Spring
Design of interactive multimedia projects. Interactive presentations and electronic publishing. Software includes hypermedia, scripting, digital output.
prereq: [[2334 or 2342], design minor] or graphic design major or grad student or instr consent
GDES 5342 - Advanced Web Design
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Every Spring
Internet-based design. Static web pages, embedded media, cascading style sheets. Design and usability of interface between humans and technology. Evaluation of visual elements that control and organize dealings with computers to direct work. Students develop designs, do usability testing.
prereq: [[2334 or 2342], design minor] or graphic design major or grad student or instr consent
GDES 5383 - Digital Illustration and Animation
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Periodic Fall & Spring
Advanced computer design. Integration of design knowledge with Macintosh computer applications. Students use software to create digital illustration and animations. Adobe Illustrator, After Effects, Flash.
prereq: [[2334 or 2342], design minor], [graphic design major or [grad student, experience with computer illustration]]] or instr consent
GDES 5386 - Fundamentals of Game Design
Credits:
3.0
[max 3.0]
Grading Basis:
A-F or Aud
Typically offered:
Periodic Fall & Spring
Games of all kinds. Theoretical/practical aspects of making games. Investigation of design process. Rules, strategies, methodologies. Interactivity, choice, action, outcome, rules in game design. Social interaction, story telling, meaning/ideology, semiotics. Signs, cultural meaning.
prereq: [[2334 or 2342], design minor] or [[4384 or DHA 4384 or 5341 or DHA 5341], [graphic design major or sr or grad student]] or instr consent
KIN 5505 - Human-Centered Design - Principles and Applications
Credits:
3.0
[max 3.0]
Course Equivalencies:
Kin 3505/Kin 5505
Grading Basis:
A-F only
Typically offered:
Every Fall
Application of design to meet human needs. Design of fabricated products, tools/machines, software/hardware interfaces, art/culture, living environments, and complex sociotechnical systems.
PDES 5701 - User-Centered Design Studio
Credits:
4.0
[max 4.0]
Grading Basis:
A-F only
Typically offered:
Every Spring
This class provides a studio-based overview of user-centered product design and development processes. Students will practice both user and market research, creativity and idea generation tools, concept evaluation/selection techniques, prototyping methods for concept development and communication, and user testing. This class will also cover fundamentals of intellectual property and manufacturing. In this studio, students will apply these skills towards the development of a product concept.
PDES 5702 - Visual Communication
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Every Fall
This class provides an overview of sketching, manual rendering and Adobe Photoshop, Illustrator, and InDesign for communication of conceptual product design. Topics covered will include free-hand perspective drawing of simple/complex geometries, line weight/quality, shading/shadow, design details and annotations, as well as image editing, vector graphics, and multi-page layout design. There will be weekly drawing assignments and critique of work.
PDES 5703 - Prototyping Methods
Credits:
4.0
[max 4.0]
Grading Basis:
A-F only
Typically offered:
Every Fall
This class is a hands-on introduction to traditional and digitally interactive prototyping tools and techniques. Through a series of projects students will gain experience with building product models using different materials and tools related to foam core, foam, wood, Arduino, and digital fabrication. In the process, the course covers design topics related to form and function, ergonomics, visual aesthetics, and design critique.
PDES 5711 - Product Innovation Lab
Credits:
4.0
[max 4.0]
Course Equivalencies:
PDes 3711/PDes 5711
Grading Basis:
A-F only
Typically offered:
Every Spring
A hands-on experience in integrated product design and development processes. Elements of industrial design, engineering, business, and humanities are applied to a semester-long product design project. Cross-functional teams of students in different majors work together to design and develop new consumer product concepts with guidance from a community of industry mentors.
EE 4951W - Senior Design Project
(WI)
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Team participation in formulating/solving open-ended design problems. Oral/written presentations.
prereq: 3015, 3115, 3102, attendance first day of class
EE 4111 - Advanced Analog Electronics Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Basic integrated circuit building blocks of differential amplifiers, high bandwidth, instrumentation amplifiers. Current/voltage references. Feedback, stability, and noise in electronic circuits. Integral lab.
prereq: 3015, 3115
EE 4163 - Energy Conversion and Storage Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Provides laboratory experiences with the topics of 4161W, including the fundamental physics and chemistry of selected energy conversion and energy storage devices, their application, and their connection strategies in electric power applications.
prereq: concurrent registration is required (or allowed) in 4161W
EE 4235 - Linear Control Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Lab to accompany 4231.
prereq: 4231 or concurrent registration is required (or allowed) in 4231
EE 4237 - State Space Control Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Lab to accompany 4233.
prereq: 4233 or concurrent registration is required (or allowed) in 4233; no cr for [EE or CompE] grad students
EE 4301 - Digital Design With Programmable Logic
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Summer
Introduction to system design/simulation. Design using Verilog code/synthesis. Emulation using Verilog code.
prereq: 2301, [1301 or CSCI 1113 or CSCI 1901]
EE 4341 - Embedded System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microcontroller interfacing for embedded system design. Exception handling/interrupts. Memory Interfacing. Parallel/serial input/output methods. System Buses and protocols. Serial Buses and component interfaces. Microcontroller Networks. Real-Time Operating Systems. Integral lab.
prereq: 2301, 2361, upper div CSE
EE 4505 - Communications Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Experiments in analysis/design of wired/wireless communication systems. Lab to accompany 4501.
prereq: 4501 or concurrent registration is required (or allowed) in 4501
EE 4703 - Electric Drives Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Laboratory to accompany 4701. Simulink-based simulations of electric machines/drives in applications such as energy conservation and motion control in robotics.
prereq: 4701 or concurrent registration is required (or allowed) in 4701
EE 4722 - Power System Analysis Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Lab analysis of AC power systems, power system networks, power flow, short circuit, transient stability.
prereq: 4721 or concurrent registration is required (or allowed) in 4721
EE 4743 - Switch-Mode Power Electronics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Laboratory to accompany 4741. PSpice-/Simulink-based simulations of converters, topologies, and control in switch-mode dc power supplies, motor drives for motion control, and inverters for interfacing renewable energy sources to utility grid.
prereq: 4741 or concurrent registration is required (or allowed) in 4741
EE 4930 - Special Topics in Electrical and Computer Engineering Laboratory
Credits:
1.0
-2.0
[max 6.0]
Grading Basis:
A-F only
Typically offered:
Periodic Fall, Spring & Summer
Lab work not available in regular courses. Topics vary.
prereq: CSE sr or grad student or instr consent
EE 5141 - Introduction to Microsystem Technology
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microelectromechanical systems composed of microsensors, microactuators, and electronics integrated onto common substrate. Design, fabrication, and operation principles. Labs on micromachining, photolithography, etching, thin film deposition, metallization, packaging, and device characterization.
prereq: [3161, 3601, CSE grad student] or dept consent
EE 5173 - Basic Microelectronics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Fall
Students fabricate a polysilicon gate, single-layer metal, NMOS chip, performing 80 percent of processing, including photolithography, diffusion, oxidation, and etching. In-process measurement results are compared with final electrical test results. Simple circuits are used to estimate technology performance.
prereq: [[5171 or concurrent registration is required (or allowed) in 5171], CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation.
prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5373 - Data Modeling Using R
Credits:
1.0
[max 1.0]
Grading Basis:
A-F only
Typically offered:
Periodic Fall & Spring
Introduction to data modeling and the R language programming. Multi-factor linear regression modeling.
Residual analysis and model quality evaluation. Response prediction. Training and testing. Integral lab.
An introductory course in probability and statistics is suggested but not required; basic programming skills in some high-level programming language, such as C/C++, Java, Fortran, etc also suggested.
EE 5545 - Digital Signal Processing Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Real-time implementation of digital signal processing (DSP) algorithms, including filtering, sample-rate conversion, and FFT-based spectral analysis. Implementation on a modern DSP Platform. Processor architecture. Arithmetic operations. Real-time processing issues. Processor limitations. Integral laboratory.
prereq: [4541, CSE grad student] or dept consent
EE 5613 - RF/Microwave Circuit Design Laboratory
Credits:
2.0
[max 2.0]
Grading Basis:
A-F only
Typically offered:
Every Spring
Scattering parameters, planar lumped circuits, transmission lines, RF/microwave substrate materials, matching networks/tuning elements, resonators, filters, combiners/dividers, couplers. Integral lab.
prereq: [[5601 or concurrent registration is required (or allowed) in 5601], CSE grad student] or dept consent
EE 5622 - Physical Optics Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Every Spring
Fundamental optical techniques. Diffraction and optical pattern recognition. Spatial/temporal coherence. Interferometry. Speckle. Coherent/incoherent imaging. Coherent image processing. Fiber Optics.
prereq: [[5621 or concurrent registration is required (or allowed) in 5621], CSE grad student] or dept consent
EE 5657 - Physical Principles of Thin Film Technology
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall
Fabrication, characterization, and application of thin film and nanostructured materials and devices. Focuses on vacuum deposition. Materials science. Hands-on, team-based labs.
EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory
Credits:
1.0
[max 1.0]
Typically offered:
Periodic Spring
Lab to accompany 5705.
prereq: 5705 or concurrent registration is required (or allowed) in 5705
EE 5811 - Biological Instrumentation
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
This course will cover the physics and technology of biological instruments. The operating principles of optical, electrical, and mechanical biosensors will be discussed, followed by transport and delivery of biomolecules to the sensors. Techniques to manufacture these sensing devices, along with microfluidic packaging, will be covered. Lectures will be complemented by lab demo sessions to give students hands-on experiences in microfluidic chip fabrication, microscopy, and particle trapping experiments.
CSCI 4511W - Introduction to Artificial Intelligence
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Typically offered:
Every Fall & Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit.
prereq: 2041 or instr consent
CSCI 5511 - Artificial Intelligence I
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Prerequisites:
[2041 or #], grad student
Typically offered:
Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language.
prereq: [2041 or instr consent], grad student
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics.
prereq: 2031 or 2033 or instr consent
EE 4389W - Introduction to Predictive Learning
(WI)
Credits:
3.0
[max 3.0]
Typically offered:
Fall Odd Year
Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies.
prereq: [3025, ECE student] or STAT 3022; computer programming or MATLAB or similar environment is recommended for ECE students
EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.
A previous course in computer architecture is suggested but not required.
EE 5351 - Applied Parallel Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Parallel programming/architecture. Application development for many-core processors. Computational thinking, types of parallelism, programming models, mapping computations effectively to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, application case studies.
prereq: [4363 or equivalent], programming experience (C/C++ preferred)
EE 5364 - Advanced Computer Architecture
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5204/EE 5364
Typically offered:
Every Fall
Instruction set architecture, processor microarchitecture. Memory and I/O systems. Interactions between computer software and hardware. Methodologies of computer design.
prereq: [[4363 or CSci 4203], CSE grad student] or dept consent
EE 5371 - Computer Systems Performance Measurement and Evaluation
Credits:
3.0
[max 3.0]
Course Equivalencies:
EE 5371/5863
Typically offered:
Periodic Fall & Spring
Tools/techniques for analyzing computer hardware, software, system performance. Benchmark programs, measurement tools, performance metrics. Deterministic/probabilistic simulation techniques, random number generation/testing. Bottleneck analysis.
prereq: [4363 or 5361 or CSci 4203 or 5201], [CSE grad student] or dept consent
EE 5393 - Circuits, Computation, and Biology
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Connections between digital circuit design and synthetic/computational biology. Probabilistic, discrete-event simulation. Timing analysis. Information-Theoretic Analysis. Feedback in digital circuits/genetic regulatory systems. Synthesizing stochastic logic and probabilistic biochemistry.
CSCI 5204 - Advanced Computer Architecture
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5204/EE 5364
Typically offered:
Every Fall
Instruction set architecture, processor microarchitecture, memory, I/O systems. Interactions between computer software and hardware. Methodologies of computer design.
prereq: 4203 or EE 4363
EE 4231 - Linear Control Systems: Designed by Input/Output Methods
Credits:
3.0
[max 3.0]
Course Equivalencies:
AEM 4321/EE 4231
Typically offered:
Every Fall
Modeling, characteristics, performance of feedback control systems. Stability, root locus, frequency response methods. Digital implementation, hardware considerations.
prereq: [3015, [upper div CSE or grad student in CSE major]] or instr consent
EE 4233 - State Space Control System Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
State space models, performance evaluation, numerical issues for feedback control. Stability, state estimation, quadratic performance. Implementation, computational issues.
prereq: [3015, upper div CSE] or instr consent
EE 4341 - Embedded System Design
Credits:
4.0
[max 4.0]
Typically offered:
Every Spring
Microcontroller interfacing for embedded system design. Exception handling/interrupts. Memory Interfacing. Parallel/serial input/output methods. System Buses and protocols. Serial Buses and component interfaces. Microcontroller Networks. Real-Time Operating Systems. Integral lab.
prereq: 2301, 2361, upper div CSE
EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.
A previous course in computer architecture is suggested but not required.
CSCI 4511W - Introduction to Artificial Intelligence
(WI)
Credits:
4.0
[max 4.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Typically offered:
Every Fall & Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit.
prereq: 2041 or instr consent
CSCI 5143 - Real-Time and Embedded Systems
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Periodic Spring
Real-time systems that require timely response by computer to external stimulus. Embedded systems in which computer is part of machine. Increasing importance of these systems in commercial products. How to control robots and video game consoles. Lecture, informal lab.
prereq: [4061 or instr consent], experience with C language
CSCI 5511 - Artificial Intelligence I
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4511W/CSci 5511
Prerequisites:
[2041 or #], grad student
Typically offered:
Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language.
prereq: [2041 or instr consent], grad student
CSCI 5512 - Artificial Intelligence II
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 5512W/CSci 5512
Typically offered:
Every Spring
Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods.
prereq: [STAT 3021, 4041] or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence.
Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5525 - Machine Learning: Analysis and Methods
Credits:
3.0
[max 3.0]
Typically offered:
Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models.
prereq: Grad student or instr consent
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics.
prereq: 2031 or 2033 or instr consent
CSCI 5552 - Sensing and Estimation in Robotics
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping.
prereq: [5551, Stat 3021] or instr consent
CSCI 5561 - Computer Vision
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications.
prereq: CSci 5511, 5521, or instructor consent.
EE 5271 - Robot Vision
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Modern visual perception for robotics that includes position and orientation, camera model and calibration, feature detection, multiple images, pose estimation, vision-based control, convolutional neural networks, reinforcement learning, deep Q-network, and visuomotor policy learning. [Math 2373 or equivalent; EE 1301 or equivalent basic programming course]
EE 4301 - Digital Design With Programmable Logic
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Summer
Introduction to system design/simulation. Design using Verilog code/synthesis. Emulation using Verilog code.
prereq: 2301, [1301 or CSCI 1113 or CSCI 1901]
EE 5301 - VLSI Design Automation I
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Basic graph/numerical algorithms. Algorithms for logic/high-level synthesis. Simulation algorithms at logic/circuit level. Physical-design algorithms.
prereq: [2301, CSE grad student] or dept consent
EE 5302 - VLSI Design Automation II
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Basic algorithms, computational complexity. High-level synthesis. Test generation. Power estimation. Timing optimization. Current topics.
prereq: [5301, CSE grad student] or dept consent
EE 5323 - VLSI Design I
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Combinational static CMOS circuits. Transmission gate networks. Clocking strategies, sequential circuits. CMOS process flows, design rules, structured layout techniques. Dynamic circuits, including Domino CMOS and DCVS. Performance analysis, design optimization, device sizing.
prereq: [2301, 3115, CSE grad student] or dept consent
EE 5324 - VLSI Design II
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
CMOS arithmetic logic units, high-speed carry chains, fast CMOS multipliers. High-speed performance parallel shifters. CMOS memory cells, array structures, read/write circuits. Design for testability, including scan design and built-in self test. VLSI case studies.
prereq: [5323, CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation.
prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5329 - VLSI Digital Signal Processing Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Programmable architectures for signal/media processing. Data-flow representation. Architecture transformations. Low-power design. Architectures for two's complement/redundant representation, carry-save, and canonic signed digit. Scheduling/allocation for high-level synthesis.
prereq: [[5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5333 - Analog Integrated Circuit Design
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Fundamental circuits for analog signal processing. Design issues associated with MOS/BJT devices. Design/testing of circuits. Selected topics (e.g., modeling of basic IC components, design of operational amplifier or comparator or analog sampled-data circuit filter).
prereq: [3115, CSE grad student] or dept consent
EE 4501 - Communications Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Systems for transmission/reception of digital/analog information. Characteristics/design of wired/wireless communication systems. Baseband, digital, and carrier-based techniques. Modulation. Coding. Electronic noise and its effects on design/performance.
prereq: 3025
CSCI 4131 - Internet Programming
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4131/CSci 5131
Typically offered:
Every Fall & Spring
Issues in internet programming. Internet history, architecture/protocols, network programming, Web architecture. Client-server architectures and protocols. Client-side programming, server-side programming, dynamic HTML, Java programming, object-oriented architecture/design, distributed object computing, Web applications.
prereq: 4061, 4211 recommended, cannot be taken for grad CSci cr
CSCI 4211 - Introduction to Computer Networks
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4211/CSci 5211/INET 4002
Typically offered:
Every Fall & Spring
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, routing, transport, network programming interfaces, networked applications. Examples from Ethernet, Token Ring, TCP/IP, HTTP, WWW.
prereq: 4061 or instr consent; basic knowledge of [computer architecture, operating systems] recommended, cannot be taken for grad CSci cr
CSCI 5117 - Developing the Interactive Web
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Hands-on design experience using modern web development tools. Students work in teams to develop software programs using each of four toolkits. Analyze developments in forum posts and classroom discussions.
prereq: 4131 or 5131 or instr consent; upper div or grad in CSci recommended
CSCI 5211 - Data Communications and Computer Networks
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4211/CSci 5211/INET 4002
Typically offered:
Every Fall
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, network layer/routing protocols, transport, congestion/flow control, emerging high-speed networks, network programming interfaces, networked applications. Case studies using Ethernet, Token Ring, FDDI, TCP/IP, ATM, Email, HTTP, and WWW.
prereq: [4061 or instr consent], basic knowledge of [computer architecture, operating systems, probability], grad student
CSCI 5221 - Foundations of Advanced Networking
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Design principles, protocol mechanisms. Network algorithmics, implementation techniques. Advanced network architectures, state-of-art/emerging networking technologies/applications, network modeling. Simulation, experiments.
prereq: 4211 or 5211 or equiv; intro course in computer networks recommended
CSCI 5271 - Introduction to Computer Security
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Concepts of computer, network, and information security.
Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls.
prereq: 4061 or 5103 or equiv or instr consent
CSCI 5471 - Modern Cryptography
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Introduction to cryptography. Theoretical foundations, practical applications. Threats, attacks, and countermeasures, including cryptosystems and cryptographic protocols. Secure systems/networks. History of cryptography, encryption (conventional, public key), digital signatures, hash functions, message authentication codes, identification, authentication, applications.
prereq: [2011, 4041, [familiarity with number theory or finite fields]] or instr consent
EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
Algorithm techniques for enhancing the scalability of parallel software: scatter-to-gather, problem decomposition, binning, privatization, tiling, regularization, compaction, double-buffering, and data layout. These techniques address the most challenging problems in building scalable parallel software: limited parallelism, data contention, insufficient memory bandwidth, load balance, and communication latency. Programming assignments will be given to reinforce the understanding of the techniques.
prereq: basic knowledge of CUDA, experience working in a Unix environment, and experience developing and running scientific codes written in C or C++. Completion of EE 5351 is not required but highly recommended.
CSCI 4011 - Formal Languages and Automata Theory
Credits:
4.0
[max 4.0]
Typically offered:
Every Fall & Spring
Logical/mathematical foundations of computer science. Formal languages, their correspondence to machine models. Lexical analysis, string matching, parsing. Decidability, undecidability, limits of computability. Computational complexity.
prereq: 2041 or instr consent
CSCI 4707 - Practice of Database Systems
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4707/CSci 5707/INET 4707
Typically offered:
Every Fall & Spring
Concepts, conceptual data models, case studies, common data manipulation languages, logical data models, database design, facilities for database security/integrity, applications.
prereq: 4041 or instr consent
CSCI 5103 - Operating Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Conceptual foundation of operating system designs and implementations. Relationships between operating system structures and machine architectures. UNIX implementation mechanisms as examples.
prereq: 4061 or instr consent
CSCI 5105 - Introduction to Distributed Systems
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Distributed system design and implementation. Distributed communication and synchronization, data replication and consistency, distributed file systems, fault tolerance, and distributed scheduling.
prereq: [5103 or equiv] or instr consent
CSCI 5106 - Programming Languages
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages.
prereq: 4011 or instr consent
CSCI 5117 - Developing the Interactive Web
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Hands-on design experience using modern web development tools. Students work in teams to develop software programs using each of four toolkits. Analyze developments in forum posts and classroom discussions.
prereq: 4131 or 5131 or instr consent; upper div or grad in CSci recommended
CSCI 5125 - Collaborative and Social Computing
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Introduction to computer-supported cooperative work, social computing. Technology, research methods, theory, case studies of group computing systems. Readings, hands-on experience.
prereq: 5115 or instr consent
CSCI 5161 - Introduction to Compilers
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Techniques for translating modern programming languages to intermediate forms or machine-executable instructions/their organization into compiler. Lexical analysis, syntax analysis, semantic analysis, data flow analysis, code generation. Compiler project for prototypical language.
prereq: [2021, 5106] or instr consent
CSCI 5271 - Introduction to Computer Security
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Concepts of computer, network, and information security.
Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls.
prereq: 4061 or 5103 or equiv or instr consent
CSCI 5421 - Advanced Algorithms and Data Structures
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains.
prereq: 4041 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.
prereq: 4041 or instr consent
CSCI 5707 - Principles of Database Systems
Credits:
3.0
[max 3.0]
Course Equivalencies:
CSci 4707/CSci 5707/INET 4707
Typically offered:
Every Fall
Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends.
prereq: [4041 or instr consent], grad student
CSCI 5708 - Architecture and Implementation of Database Management Systems
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules.
prereq: 4041 or 4707 or 5707 or instr. consent
CSCI 5801 - Software Engineering I
Credits:
3.0
[max 3.0]
Prerequisites:
2041 or #
Typically offered:
Every Fall
Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance.
prereq: 2041 or instr consent
CSCI 5802 - Software Engineering II
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Spring
Introduction to software testing, software maturity models, cost specification models, bug estimation, software reliability models, software complexity, quality control, and experience report. Student groups specify, design, implement, and test partial software systems. Application of general software development methods and principles from 5801.
prereq: 5801 or instr consent
CSCI 5302 - Analysis of Numerical Algorithms
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Additional topics in numerical analysis. Interpolation, approximation, extrapolation, numerical integration/differentiation, numerical solutions of ordinary differential equations. Introduction to optimization techniques.
prereq: 2031 or 2033 or instr consent
CSCI 5304 - Computational Aspects of Matrix Theory
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Perturbation theory for linear systems and eigenvalue problems. Direct/iterative solution of large linear systems. Matrix factorizations. Computation of eigenvalues/eigenvectors. Singular value decomposition. LAPACK/other software packages. Introduction to sparse matrix methods.
prereq: 2031 or 2033 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.
prereq: 4041 or instr consent
CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Computational methods for analyzing, integrating, and
deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets.
prereq: 3003 or 4041 or instr consent
CSCI 5481 - Computational Techniques for Genomics
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks.
prereq: 4041 or instr consent
CSCI 5523 - Introduction to Data Mining
Credits:
3.0
[max 3.0]
Typically offered:
Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects.
prereq: 4041 or equiv or instr consent
CSCI 5609 - Visualization
Credits:
3.0
[max 3.0]
Typically offered:
Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization.
prereq: [1913, 4041] or equiv or instr consent
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
Credits:
3.0
[max 3.0]
Typically offered:
Spring Even Year
Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc.
prereq: Familiarity with Java, C++, or Python
CSCI 4611 - Programming Interactive Computer Graphics and Games
Credits:
3.0
[max 3.0]
Typically offered:
Every Spring
Tools/techniques for programming games/interactive computer graphics. Event loops, rendering/animation, polygonal models, texturing, physical simulation. Modern graphics toolkits. History/future of computer games technology. Social impact of interactive computer graphics.
prereq: 2021 or instr consent
CSCI 5115 - User Interface Design, Implementation and Evaluation
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project.
prereq: 4041 or instr consent
In this course, you will learn and apply the principles of embodied computing to human-centered challenges. Through a semester-long team project, you will learn and demonstrate mastery of human-centered embodied computing through two phases: (1) investigating human needs and current embodied practices and (2) rapidly prototyping and iterating embodied computing solutions. One of the ways you will demonstrate this mastery is through the collaborative creation of a written document and project capstone video describing your process and prototype.
prereq: CSci 4041, upper division or graduate student, or instructor permission; CSci 5115 or equivalent recommended.
CSCI 5607 - Fundamentals of Computer Graphics 1
Credits:
3.0
[max 3.0]
Typically offered:
Every Fall
Fundamental algorithms in computer graphics. Emphasizes programming projects in C/C++. Scan conversion, hidden surface removal, geometrical transformations, projection, illumination/shading, parametric cubic curves, texture mapping, antialising, ray tracing. Developing graphics software, graphics research.
prereq: concurrent registration is required (or allowed) in 2033, concurrent registration is required (or allowed) in 3081
CSCI 5611 - Animation & Planning in Games
Credits:
3.0
[max 3.0]
Typically offered:
Fall Odd Year
Theory behind algorithms used to bring virtual worlds to life. Computer animation topics. Real-time, interactive techniques used in modern games. Physically-based animation, motion planning, character animation, simulation in virtual worlds.
prereq: 4041 or 4611 or instr consent
CSCI 5619 - Virtual Reality and 3D Interaction
Credits:
3.0
[max 3.0]
Typically offered:
Spring Odd Year
Introduction to software, technology/applications in virtual/augmented reality, 3D user interaction. Overview of current research. Hands-on projects.
prereq: 4611 or 5607 or 5115 or equiv or instr consent
