Twin Cities campus
 
Twin Cities Campus

Computer Science B.S. Comp.Sc.

Computer Science and Engineering
College of Science and Engineering
  • Program Type: Baccalaureate
  • Requirements for this program are current for Spring 2020
  • Required credits to graduate with this degree: 120
  • Required credits within the major: 77 to 78
  • Degree: Bachelor of Science in Computer Science
Computer science is concerned with the study of hardware, software, and theoretical aspects of high-speed computing devices and with the application of these devices to scientific, technological, and business problems. A bachelor's degree gives students a basic understanding of computer science. After completing a required set of fundamental courses, students arrange their subsequent work around one of several upper division tracks within either computer science or an interdisciplinary area involving computer applications. The degree prepares students for graduate work or for various industrial, governmental, and business positions involving the use of computers.
Program Delivery
This program is available:
  • via classroom (the majority of instruction is face-to-face)
Admission Requirements
Students must complete 5 courses before admission to the program.
Freshman and transfer students are usually admitted to pre-major status before admission to this major.
For information about University of Minnesota admission requirements, visit the Office of Admissions website.
Required prerequisites
Mathematics Core
MATH 1371 - CSE Calculus I [MATH] (4.0 cr)
or MATH 1271 - Calculus I [MATH] (4.0 cr)
or MATH 1571H - Honors Calculus I [MATH] (4.0 cr)
MATH 1372 - CSE Calculus II (4.0 cr)
or MATH 1272 - Calculus II (4.0 cr)
or MATH 1572H - Honors Calculus II (4.0 cr)
Required prerequisites
Computer Science Introductory Core
CSCI 2011 - Discrete Structures of Computer Science (4.0 cr)
Options
Option 1
CSCI 1133 - Introduction to Computing and Programming Concepts (4.0 cr)
CSCI 1933 - Introduction to Algorithms and Data Structures (4.0 cr)
or Option 2
CSCI 1103 - Introduction to Computer Programming in Java (4.0 cr)
or CSCI 1113 - Introduction to C/C++ Programming for Scientists and Engineers (4.0 cr)
CSCI 1913 - Introduction to Algorithms, Data Structures, and Program Development (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.
Science Core
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)
or PHYS 1501V - Honors Introduction to Mechanics [PHYS, WI] (4.0 cr)
Take 1 or more course(s) from the following:
· ESCI 2201 - Solid Earth Dynamics (4.0 cr)
· GCD 3022 - Genetics (3.0 cr)
· PHYS 1302W - Introductory Physics for Science and Engineering II [PHYS, WI] (4.0 cr)
· PHYS 1402V - Honors Physics II [PHYS, WI] (4.0 cr)
· PHYS 1502V - Honors Introduction to Electricity and Magnetism [PHYS, WI] (4.0 cr)
· PSY 3011 - Introduction to Learning and Behavior (3.0 cr)
· Chemistry 1
· CHEM 1061 - Chemical Principles I [PHYS] (3.0 cr)
CHEM 1065 - Chemical Principles I Laboratory [PHYS] (1.0 cr)
· Chemistry 1 Honors
· CHEM 1071H - Honors Chemistry I [PHYS] (3.0 cr)
CHEM 1075H - Honors Chemistry I Laboratory [PHYS] (1.0 cr)
· Chemistry 2
· CHEM 1062 - Chemical Principles II [PHYS] (3.0 cr)
CHEM 1066 - Chemical Principles II Laboratory [PHYS] (1.0 cr)
· Chemistry 2 Honors
· CHEM 1072H - Honors Chemistry II [PHYS] (3.0 cr)
CHEM 1076H - Honors Chemistry II Laboratory [PHYS] (1.0 cr)
Computer Science Core
CSCI 2041 - Advanced Programming Principles (4.0 cr)
CSCI 3081W - Program Design and Development [WI] (4.0 cr)
CSCI 4041 - Algorithms and Data Structures (4.0 cr)
CSCI 4061 - Introduction to Operating Systems (4.0 cr)
Linear Algebra
CSCI 2033 - Elementary Computational Linear Algebra (4.0 cr)
or MATH 2142 - Elementary Linear Algebra (1.0-4.0 cr)
or Acceptable Substitutions with MATH 4242
MATH 4242 - Applied Linear Algebra (4.0 cr)
Acceptable Substitutions
MATH 2243 - Linear Algebra and Differential Equations (4.0 cr)
or MATH 2373 - CSE Linear Algebra and Differential Equations (4.0 cr)
or MATH 2471 - UM Talented Youth Mathematics Program--Calculus II, Second Semester [MATH] (2.0 cr)
or MATH 2574H - Honors Calculus IV (4.0 cr)
or Acceptable Honors Math Substitutions
MATH 3592H - Honors Mathematics I (5.0 cr)
MATH 3593H - Honors Mathematics II (5.0 cr)
Statistics
STAT 3021 - Introduction to Probability and Statistics (3.0 cr)
or IE 3521 - Statistics, Quality, and Reliability (4.0 cr)
or EE 3025 - Statistical Methods in Electrical and Computer Engineering (3.0 cr)
or Acceptable Substitutions with Stat 3011
STAT 3011 - Introduction to Statistical Analysis [MATH] (4.0 cr)
Acceptable Substitutions
STAT 3022 - Data Analysis (4.0 cr)
or STAT 4101 - Theory of Statistics I (4.0 cr)
or STAT 4102 - Theory of Statistics II (4.0 cr)
or STAT 5101 - Theory of Statistics I (4.0 cr)
or STAT 5102 - Introduction to Statistical Learning (4.0 cr)
or MATH 4653 - Elementary Probability (4.0 cr)
or MATH 5651 - Basic Theory of Probability and Statistics (4.0 cr)
Computer Architecture
CSCI 2021 - Machine Architecture and Organization (4.0 cr)
or EE 2361 - Introduction to Microcontrollers (4.0 cr)
Computer Science Major Electives
Students are strongly encouraged to talk with an academic advisor about faculty constructed tracks suggested within the major electives to complete a specialization within computer science. Students are required to complete 23 credits. Of the 23 credits, 11 must have a CSCI designator.
Take 23 or more credit(s) from the following:
Upper Division Math Oriented Requirement
The following MATH courses are not accepted: MATH 4005, 4065, 4067W, 4113, 4116, 4118, 5067, 5068, 5075, and 5076.
Take 1 or more course(s) from the following:
· CSCI 4011 - Formal Languages and Automata Theory (4.0 cr)
· CSCI 5302 - Analysis of Numerical Algorithms (3.0 cr)
· CSCI 5304 - Computational Aspects of Matrix Theory (3.0 cr)
· CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
· CSCI 5471 - Modern Cryptography (3.0 cr)
· CSCI 5481 - Computational Techniques for Genomics (3.0 cr)
· CSCI 5525 - Machine Learning (3.0 cr)
· MATH 4xxx
· MATH 5xxx
· Computer Science Electives
Take 19 or more credit(s) from the following:
· CSCI 4xxx
· CSCI 5xxx
· AEM 4601 - Instrumentation Laboratory (3.0 cr)
· AEM 4602W - Aeromechanics Laboratory [WI] (4.0 cr)
· AST 4041 - Computational Methods in the Physical Sciences (4.0 cr)
· BIOL 5272 - Applied Biostatistics (4.0 cr)
· CHEM 4021 - Computational Chemistry (3.0 cr)
· EE 4301 - Digital Design With Programmable Logic (4.0 cr)
· EE 4303 - Introduction to Programmable Devices Laboratory (1.0 cr)
· EE 4341 - Embedded System Design (4.0 cr)
· EE 4363 - Computer Architecture and Machine Organization (4.0 cr)
· EE 5364 - Advanced Computer Architecture (3.0 cr)
· EE 5371 - Computer Systems Performance Measurement and Evaluation (3.0 cr)
· EE 5393 - Circuits, Computation, and Biology (3.0 cr)
· EE 5505 - Wireless Communication (3.0 cr)
· FNRM 5131 - Geographical Information Systems (GIS) for Natural Resources (4.0 cr)
· FNRM 5262 - Remote Sensing and Geospatial Analysis of Natural Resources and Environment (3.0 cr)
· FNRM 5462 - Advanced Remote Sensing and Geospatial Analysis (3.0 cr)
· HSCI 4321 - History of Computing [TS, HIS] (3.0 cr)
· IDSC 4204W - Strategic Information Technology Management [WI] (4.0 cr)
· IDSC 4431 - Advanced Database Design (2.0 cr)
· IDSC 4441 - Electronic Commerce (2.0 cr)
· IE 4011 - Stochastic Models (4.0 cr)
· INET 4011 - Networking I: Network Administration (4.0 cr)
· INET 4021 - Dev Ops I: Network Programming (4.0 cr)
· INET 4041 - Networking II: Emerging Technologies (4.0 cr)
· INET 4061 - Data Science I: Fundamentals (4.0 cr)
· INET 4710 - Data Science II: Big Data Analytics (4.0 cr)
· INET 4711 - Data Management II: Distributed Systems (4.0 cr)
· KIN 5001 - Foundations of Human Factors/Ergonomics (3.0 cr)
· LING 5801 - Introduction to Computational Linguistics (3.0 cr)
· MATH 4xxx
· MATH 5xxx
· ME 5228 - Introduction to Finite Element Modeling, Analysis, and Design (4.0 cr)
· ME 5286 - Robotics (4.0 cr)
· MICE 5035 - Personal Microbiome Analysis (3.0 cr)
· PHYS 4041 - Computational Methods in the Physical Sciences (4.0 cr)
· PHYS 4051 - Methods of Experimental Physics I (5.0 cr)
· PSY 5018H - Mathematical Models of Human Behavior (3.0 cr)
· PSY 5038W - Introduction to Neural Networks [WI] (3.0 cr)
· STAT 4xxx
· STAT 5xxx
· GDES and PDES course options
Take 0 - 2 course(s) from the following:
· GDES 4371 - Data Visualization Studio (3.0 cr)
· GDES 5341 - Interaction Design (3.0 cr)
· GDES 5342 - Advanced Web Design (3.0 cr)
· GDES 5372 - Data Visualization for Interactive Platforms (3.0 cr)
· GDES 5386 - Fundamentals of Game Design (3.0 cr)
· PDES 5704 - Computer-Aided Design Methods (3.0 cr)
Upper Division Writing Intensive within the major
Students are required to take one upper division writing intensive course within the major. If that requirement has not been satisfied within the core major requirements, students must choose one course from the following list. Some of these courses may also fulfill other major requirements.
Take 0 - 1 course(s) from the following:
· CSCI 3081W - Program Design and Development [WI] (4.0 cr)
· CSCI 3921W - Social, Legal, and Ethical Issues in Computing [CIV, WI] (3.0 cr)
· CSCI 4271W - Development of Secure Software Systems [WI] (4.0 cr)
· CSCI 4511W - Introduction to Artificial Intelligence [WI] (4.0 cr)
· CSCI 4970W - Advanced Project Laboratory [WI] (3.0 cr)
· CSCI 5127W - Embodied Computing: Design & Prototyping [WI] (3.0 cr)
Program Sub-plans
A sub-plan is not required for this program.
Integrated Computer Science B.S./M.S. Program
The Department of Computer Science & Engineering offers an integrated Bachelorís and Masterís 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 Masterís 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 Masterís 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 Masterís 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 ďdouble dippingĒ). 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. Courses to be used for MS Program must be moved into the subplan prior to the tenth day of the semester in which the student is enrolled.
 
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· College of Science and Engineering

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· CSci BS Plan
· Integrated B.S./M.S. Sample Plan

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· Computer Science B.S. Comp.Sc.
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MATH 1371 - CSE Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 1142/1271/1281/1371/1571H
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 1271 - Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00067 - Math 1271/Math 1281/Math 1371/
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 1571H - Honors Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00067 - Math 1142/1271/1281/1371/1571H
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 1372 - CSE Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 1272/1282/1252/1372/1572
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 1272 - Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 1272/1282/1252/1372/1572
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 1572H - Honors Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: 00068 - Math 1272/1282/1252/1372/1572
Grading Basis: A-F only
Typically offered: Every Spring
Continuation of 1571. Infinite series, differential calculus of several variables, introduction to linear algebra. prereq: 1571H, honors student, permission of University Honors Program
CSCI 2011 - Discrete Structures of Computer Science
Credits: 4.0 [max 4.0]
Course Equivalencies: 02004
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
CSCI 1133 - Introduction to Computing and Programming Concepts
Credits: 4.0 [max 4.0]
Course Equivalencies: 02133 - 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 1933 - Introduction to Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Course Equivalencies: 00008
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
CSCI 1103 - Introduction to Computer Programming in Java
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Fundamental programming concepts/software development using Java language. Problem solving skills. Algorithm development techniques. Use of abstractions/modularity. Data structures/abstract data types. Substantial programming projects. Weekly lab.
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. prereq: Math 1271 or Math 1371 or 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
PHYS 1301W - Introductory Physics for Science and Engineering I (PHYS, WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00078
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: 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 1571
PHYS 1401V - Honors Physics I (PHYS, WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00078
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.
PHYS 1501V - Honors Introduction to Mechanics (PHYS, WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00078 - Phys 1201W/Phys1301W/Phys1401V
Grading Basis: A-F only
Typically offered: Every Fall
Principles of mechanics for those with full year of calculus/equivalent of one year of high-school physics. Introduction to kinematics, forces, momentum/energy, conservation laws, angular momentum, rigid body motion, gravity, simple harmonic motion, waves. prereq: One year of high school physics, one year of college-level calculus (or a grade of 5 on the BC-level AP calculus exam), Honors, permission of UHP
ESCI 2201 - Solid Earth Dynamics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Dynamics of solid Earth, particularly tectonic system. Seismology, internal structure of Earth. Earth's gravity, magnetic fields. Paleomagnetism, global plate tectonics, tectonic systems. Field trip. prereq: concurrent registration is required (or allowed) in PHYS 1301 or instr consent
GCD 3022 - Genetics
Credits: 3.0 [max 3.0]
Course Equivalencies: 00052
Typically offered: Every Fall, Spring & Summer
Mechanisms of heredity, implications for biological populations. Applications to practical problems. prereq: Introductory biology course such as Biol 1009
PHYS 1302W - Introductory Physics for Science and Engineering II (PHYS, WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00079
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: 1301W, concurrent registration is required (or allowed) in Math 1272 or Math 1372 or Math 1572
PHYS 1402V - Honors Physics II (PHYS, WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00079
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. prereq: 1401V, honors student or permission of University Honors Program
PHYS 1502V - Honors Introduction to Electricity and Magnetism (PHYS, WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00079 - Phys 1202W/1302W/1402V
Grading Basis: A-F only
Typically offered: Every Spring
Principles of electricity/magnetism for those with full year of calculus/equivalent of one year of high-school physics. Introduction to electrostatics, magnetostatics, electrodynamics, electric/magnetic properties of materials, circuits, Maxwell's equations, electromagnetic waves. prereq: 1501V, honors
PSY 3011 - Introduction to Learning and Behavior
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Methods/findings of research on learning and behavior change. Twentieth-century theoretical perspectives, including contemporary models. Emphasizes animal learning and behavioral psychology. prereq: 1001
CHEM 1061 - Chemical Principles I (PHYS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 01884 - Chem 1061/Chem 1071H
Typically offered: Every Fall, Spring & Summer
Atomic theory, periodic properties of elements. Thermochemistry, reaction stoichiometry. Behavior of gases, liquids, and solids. Molecular/ionic structure/bonding. Organic chemistry and polymers. energy sources, environmental issues related to energy use. Prereq-Grade of at least C- in [1011 or 1015] or [passing placement exam, concurrent registration is required (or allowed) in 1065]; intended for science or engineering majors; concurrent registration is required (or allowed) in 1065; registration for 1065 must precede registration for 1061
CHEM 1065 - Chemical Principles I Laboratory (PHYS)
Credits: 1.0 [max 1.0]
Course Equivalencies: 01878 - Chem 1065/Chem 1075H
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 1061
CHEM 1071H - Honors Chemistry I (PHYS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 01884 - Chem 1061/Chem 1071H
Grading Basis: A-F only
Typically offered: Every Fall
Advanced introduction to atomic theory. Periodic properties of elements. Behavior of gases, liquids, and solids. Molecular/ionic structure, bonding. Aspects of organic chemistry, spectroscopy, and polymers. Mathematically demanding quantitative problems. Writing for scientific journals. prereq: Honors student, permission of University Honors Program, concurrent registration is required (or allowed) in 1075H; registration for 1075H must precede registration for 1071H
CHEM 1075H - Honors Chemistry I Laboratory (PHYS)
Credits: 1.0 [max 1.0]
Course Equivalencies: 01878 - Chem 1065/Chem 1075H
Grading Basis: A-F only
Typically offered: Every Fall
Develop laboratory skills while investigating physical and chemical phenomena closely linked to lecture material. Experimental design, data collection and treatment, discussion of errors, and the proper treatment of hazardous wastes. Prereq-&1071H, honors student, permission of University Honors Program.
CHEM 1062 - Chemical Principles II (PHYS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 01885 - Chem 1062/Chem 1072H
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: 01879 - 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 1072H - Honors Chemistry II (PHYS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 01885 - Chem 1062/Chem 1072H
Grading Basis: A-F only
Typically offered: Every Spring
Advanced introduction. Chemical kinetics/reaction mechanisms, chemical/physical equilibria, acids/bases, entropy/second law of thermodynamics, electrochemistry/corrosion; descriptive chemistry of elements; coordination chemistry; biochemistry. prereq: 1071H, concurrent registration is required (or allowed) in 1076H, honors student, registration for 1076H must precede registration for 1072H
CHEM 1076H - Honors Chemistry II Laboratory (PHYS)
Credits: 1.0 [max 1.0]
Course Equivalencies: 01879 - Chem 1066/Chem 1076H
Grading Basis: A-F only
Typically offered: Every Spring
Develop laboratory skills as experiments become increasingly complex. Data collection/treatment, discussion of errors, proper treatment of hazardous wastes, experiment design. prereq: concurrent registration is required (or allowed) in 1072H
CSCI 2041 - Advanced Programming Principles
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Principles/techniques for creating correct, robust, modular programs. Computing with symbolic data, recursion/induction, functional programming, impact of evaluation strategies, parallelism. Organizing data/computations around types. Search-based programming, concurrency, modularity. prereq: [1913 or 1933], 2011
CSCI 3081W - Program Design and Development (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00728 - CSci 3081W/CSci 4018W/CSci4089
Typically offered: Every Fall & Spring
Principles of programming design/analysis. Concepts in software development. Uses C/C++ language to illustrate key ideas in program design/development, data structures, debugging, files, I/O, state machines, testing, coding standards. prereq: [2021, 2041]; CS upper div, CS grad, or dept. permission
CSCI 4041 - Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Course Equivalencies: 02015
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: 02667
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, CS minor, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci.
CSCI 2033 - Elementary Computational Linear Algebra
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Matrices/linear transformations, basic theory. Linear vector spaces. Inner product spaces. Systems of linear equations, Eigenvalues, singular values. Algorithms/computational matrix methods using MATLAB. Use of matrix methods to solve variety of computer science problems. prereq: [MATH 1271 or MATH 1371], [1113 or 1133 or knowledge of programming concepts]
MATH 2142 - Elementary Linear Algebra
Credits: 1.0 -4.0 [max 1.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Not taught: merely provides credit for transfer students who have taken a sophomore-level linear algebra course that does not contain enough differential equations to qualify for credit for 2243.
MATH 4242 - Applied Linear Algebra
Credits: 4.0 [max 4.0]
Course Equivalencies: 01212 - Math 4242/Math 4457
Typically offered: Every Fall, Spring & Summer
Systems of linear equations, vector spaces, subspaces, bases, linear transformations, matrices, determinants, eigenvalues, canonical forms, quadratic forms, applications. prereq: 2243 or 2373 or 2573
MATH 2243 - Linear Algebra and Differential Equations
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 2243/2373/2573
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 2243/2373/2573
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 2471 - UM Talented Youth Mathematics Program--Calculus II, Second Semester (MATH)
Credits: 2.0 [max 4.0]
Course Equivalencies: 00561 - Math 2243/Math 2373/Math 2574H
Grading Basis: A-F or Aud
Typically offered: Every Spring
Accelerated honors sequence for selected mathematically talented high school students. Theoretical and geometric linear algebra.
MATH 2574H - Honors Calculus IV
Credits: 4.0 [max 4.0]
Course Equivalencies: 00561 - Math 2243/Math 2373/Math 2573H
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
MATH 3592H - Honors Mathematics I
Credits: 5.0 [max 5.0]
Grading Basis: A-F only
Typically offered: Every Fall
First semester of three-semester sequence. Focuses on multivariable calculus at deeper level than regular calculus offerings. Rigorous introduction to sequences/series. Theoretical treatment of multivariable calculus. Strong introduction to linear algebra. prereq: dept consent; for students with mathematical talent
MATH 3593H - Honors Mathematics II
Credits: 5.0 [max 5.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Second semester of three-semester sequence. Focuses on multivariable calculus at deeper level than regular calculus offerings. Rigorous introduction to sequences/series. Theoretical treatment of multivariable calculus. Strong introduction to linear algebra. prereq: 3592H or instr consent
STAT 3021 - Introduction to Probability and Statistics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall, Spring & Summer
This is an introductory course in statistics whose primary objectives are to teach students the theory of elementary probability theory and an introduction to the elements of statistical inference, including testing, estimation, and confidence statements. prereq: Math 1272
IE 3521 - Statistics, Quality, and Reliability
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Random variables/probability distributions, statistical sampling/measurement, statistical inferencing, confidence intervals, hypothesis testing, single/multivariate regression, design of experiments, statistical quality control, quality management, reliability, maintainability. prereq: MATH 1372 or equiv
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
STAT 3011 - Introduction to Statistical Analysis (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: (Select a set)
Typically offered: Every Fall, Spring & Summer
Standard statistical reasoning. Simple statistical methods. Social/physical sciences. Mathematical reasoning behind facts in daily news. Basic computing environment.
STAT 3022 - Data Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Practical survey of applied statistical inference/computing covering widely used statistical tools. Multiple regression, variance analysis, experiment design, nonparametric methods, model checking/selection, variable transformation, categorical data analysis, logistic regression. prereq: 3011 or 3021 or SOC 3811
STAT 4101 - Theory of Statistics I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Random variables/distributions. Generating functions. Standard distribution families. Data summaries. Sampling distributions. Likelihood/sufficiency. prereq: Math 1272 or Math 1372 or Math 1572H
STAT 4102 - Theory of Statistics II
Credits: 4.0 [max 4.0]
Course Equivalencies: 00260
Typically offered: Every Spring
Estimation. Significance tests. Distribution free methods. Power. Application to regression and to analysis of variance/count data. prereq: STAT 4101
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], [CSCI 2033 or Math 2373 or Math 2243]
STAT 5102 - Introduction to Statistical Learning
Credits: 4.0 [max 4.0]
Course Equivalencies: 00260
Typically offered: Every 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]
MATH 4653 - Elementary Probability
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Probability spaces, distributions of discrete/continuous random variables, conditioning. Basic theorems, calculational methodology. Examples of random sequences. Emphasizes problem-solving. prereq: [2263 or 2374 or 2573]; [2283 or 2574 or 3283] recommended
MATH 5651 - Basic Theory of Probability and Statistics
Credits: 4.0 [max 4.0]
Course Equivalencies: 00259 - MATH 4653/Math 5651/Stat 5101
Typically offered: Every Fall & Spring
Logical development of probability, basic issues in statistics. Probability spaces, random variables, their distributions/expected values. Law of large numbers, central limit theorem, generating functions, sampling, sufficiency, estimation. prereq: [2263 or 2374 or 2573], [2243 or 2373]; [2283 or 2574 or 3283] recommended.
CSCI 2021 - Machine Architecture and Organization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Introduction to hardware/software components of computer system. Data representation, boolean algebra, machine-level programs, instruction set architecture, processor organization, memory hierarchy, virtual memory, compiling, linking. Programming in C. prereq: 1913 or 1933 or instr consent
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: 2301, [1301 or CSCI 1113 or CSCI 1901]
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 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 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 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
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 5525 - Machine Learning
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
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]
AEM 4602W - Aeromechanics Laboratory (WI)
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Experimental methods/design in fluid/solid mechanics. Wind tunnel/water channel experiments with flow visualization, pressure, velocity, force measurements. Measurement of stresses/strains/displacements in solids/ structures: stress concentrations, materials behavior, structural dynamics. Computerized data acquisition/analysis, error analysis, data reduction. Experiment design. Written/oral reports. Lab ethics. Writing intensive. prereq: 4201, 4501, 4601, [WRIT 1301 or equiv], [CSE upper div or grad]
AST 4041 - Computational Methods in the Physical Sciences
Credits: 4.0 [max 4.0]
Course Equivalencies: 01838 - Ast 4041/Phys 4041
Typically offered: Periodic Fall & Spring
Introduction to using computer programs to solve problems in physical sciences. Selected numerical methods, mapping problems onto computational algorithms. Arranged lab. prereq: Upper div or grad student or instr consent
BIOL 5272 - Applied Biostatistics
Credits: 4.0 [max 3.0]
Course Equivalencies: 01933 - Biol 3272Biol 3272H//Biol 5272
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Conceptual basis of statistical analysis. Statistical analysis of biological data. Data visualization, descriptive statistics, significance tests, experimental design, linear model, simple/multiple regression, general linear model. Lectures, computer lab. prereq: High school algebra; BIOL 2003 recommended.
CHEM 4021 - Computational Chemistry
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Theoretical methods for study of molecular structure, bonding, and reactivity. Ab initio/semi-empirical calculations. Theoretical determination of molecular electronic structure/spectra, relation to experimental techniques. Molecular mechanics. Structure determination for large systems. Molecular properties/reactivity. Computational tools. Critical assessment of methods/theoretical work in the literature. Lab. prereq: [4502 or equiv], instr 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 4303 - Introduction to Programmable Devices Laboratory
Credits: 1.0 [max 1.0]
Course Equivalencies: 01806 - EE 4301/EE 4303
Typically offered: Periodic Spring
Verilog Language. Combinatorial and sequential logic synthesis with Verilog. Implementation in Field Programmable Gate Arrays (FPGAs). prereq: 2301, 2361; cannot receive cr for 4303 if cr granted for EE 4301
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 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
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. prereq: MATH 2263 or dept consent
EE 5505 - Wireless Communication
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to wireless communication systems. Propagation modeling, digital communication over fading channels, diversity and spread spectrum techniques, radio mobile cellular systems design, performance evaluation. Current European, North American, and Japanese wireless networks. prereq: [4501, CSE grad student] or dept consent; 5501 recommended
FNRM 5131 - Geographical Information Systems (GIS) for Natural Resources
Credits: 4.0 [max 4.0]
Course Equivalencies: 00369
Grading Basis: A-F or Aud
Typically offered: Every Fall
Geographic information systems (GIS), focusing on spatial data development and analysis in the science and management of natural resources. Basic data structures, sources, collection, and quality; geodesy and map projections; spatial and tabular data analyses; digital elevation data and terrain analyses; cartographic modeling and layout. Lab exercises provide practical experiences complementing theory covered in lecture. prereq: Grad student or instr consent
FNRM 5262 - Remote Sensing and Geospatial Analysis of Natural Resources and Environment
Credits: 3.0 [max 3.0]
Course Equivalencies: 00372 - FNRM 3262/FNRM 5262
Typically offered: Every Fall
Introductory principles and techniques of remote sensing and geospatial analysis applied to mapping and monitoring land and water resources from local to global scales. Examples of applications include: Land cover mapping and change detection, forest and natural resource inventory, water quality monitoring, and global change analysis. The lab provides hands-on experience working with satellite, aircraft, and drone imagery, and image processing methods and software. Prior coursework in Geographic Information Systems and introductory Statistics is recommended. prereq: Grad student or instr consent
FNRM 5462 - Advanced Remote Sensing and Geospatial Analysis
Credits: 3.0 [max 6.0]
Course Equivalencies: 02660 - FNRM 3462/FNRM 5462
Typically offered: Every Spring
This course builds on the introductory remote sensing class, FNRM 3262/5262. It provides a detailed treatment of advanced remote sensing and geospatial theory and methods including Object-Based Image Analysis (OBIA), lidar processing and derivatives, advanced classification algorithms (including Random Forest, Neural Networks, Support Vector Machines), biophysics of remote sensing, measurements and sensors, data transforms, data fusion, multi-temporal analysis, and empirical modeling. In-class and independent lab activities will be used to apply the course topics to real-world problems. Prior coursework in Geographic Information Systems, remote sensing, and statistics is necessary. Prereq: grad student or instr consent
HSCI 4321 - History of Computing (TS, HIS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 00497 - CSci 4921/HSci 4321
Typically offered: Fall Even, Spring Odd Year
Developments in the last 150 years; evolution of hardware and software; growth of computer and semiconductor industries and their relation to other business areas; changing relationships resulting from new data-gathering and analysis techniques; automation; social and ethical issues.
IDSC 4204W - Strategic Information Technology Management (WI)
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Information services as service function. Investing resources to support strategy. Managing IS resources. Project Management, Human Capital Management, Infrastructure Management. Emphasis on cloud/big data infrastructures, outsourcing.
IDSC 4431 - Advanced Database Design
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Reviews ER/relational modeling and normalization, then focuses on fact modeling (ORM) to produce advanced richer business data models. "Flipped" class, fully online, including all lectures & final exam. Weekly in-class review session is recorded and online for questions, discussion, and results of assignments & quizzes. prereq: 3103 or CSCI 4707 or CSCI 5707 or instr consent
IDSC 4441 - Electronic Commerce
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall, Spring & Summer
Issues/trends in applying e-commerce initiatives. Technological infrastructure, revenue models, web marketing, business-to-business strategies, online auctions, legal and ethical aspects, hardware/software, payment systems, security. Conceiving, planning, building, and managing e-commerce initiatives. prereq: 3001
IE 4011 - Stochastic Models
Credits: 4.0 [max 4.0]
Prerequisites: 3521, MATH 2373, MATH 2374, ISyE major
Grading Basis: A-F only
Typically offered: Every Spring
Models for describing/evaluating random systems. Formulating/analyzing stochastic models for business. Discrete-time/continuous-time Markov chains. Poisson processes. Markovian/non-Markovian queueing theory. Inventory management, manufacturing, reliability. prereq: 3521, MATH 2373, MATH 2374, ISyE major
INET 4011 - Networking I: Network Administration
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
A combination of networking theory (lecture and expert guest speakers) and application (lab work). Topics include network architecture, switching, routing, algorithms, protocols, infrastructure hardware, cable plant, security, and network management. prereq: CSCI 4211-Introduction to Computer Networks or equivalent networking knowledge and understanding.
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.
INET 4041 - Networking II: Emerging Technologies
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Emerging networking concepts, technologies, and applications. Topics will evolve to reflect current trends and expertise of the faculty, such as high speed networking, ATM, network security, wireless networks, multimedia, and electronic commerce. Each technology is considered for the underlying theory; the driving technological and business needs; the applications; the competing alternative technologies; and the design, implementation, and configuration of such systems. Case studies may be used to identify and analyze strategic issues and problems. Concepts and tools from this and previous ITI courses are applied to solve these problems and design realistic programs of action. Hands-on labs are included when possible. Industry speakers, tours, and demonstrations show practical applications. prereq: CSci 4211 or equivalent, or professional experience, to comprise a basic understanding and knowledge of operating systems, computer architecture, and probability theory. Senior status preferred.
INET 4061 - Data Science I: Fundamentals
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduction to data science. Design strategies for business analytics: statistics for machine learning, core data mining models, data pipeline, visualization. Hands-on labs with data mining, statistics, and in-memory analytics software. prereq: Basic statistics and programming skills, laptop
INET 4710 - Data Science II: Big Data Analytics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Scales machine learning models and data analysis to a Big Data platform. Map Reduce and Spark frameworks are introduced as approaches to parallel algorithm development. Hands-on labs. Prerequisites: Basic programming knowledge (Java, Python, R). Linear algebra strongly recommended, especially matrix operations (e.g., MATH 2243, Linear Algebra and Differential Equations)
INET 4711 - Data Management II: Distributed Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Introduction to distributed programming and systems concepts in high-scale environments with a focus on application to commercial systems in the data center. Discussion of key protocols and algorithms as well as best-practice implementations on platforms commonly associated with big data in the enterprise. Hands-on experience in the design and engineering of distributed systems on cloud-oriented technologies. prereq: INET 4031 and 4707 or consent of instructor.
KIN 5001 - Foundations of Human Factors/Ergonomics
Credits: 3.0 [max 3.0]
Course Equivalencies: HumF/Kin 5001
Grading Basis: A-F or Aud
Typically offered: Every Fall
Variability in human performance as influenced by interaction with designs of machines and tools, computers and software, complex technological systems, jobs and working conditions, organizations, and sociotechnical institutions. Emphasizes conceptual, empirical, practical aspects of human factors/ergonomic science.
LING 5801 - Introduction to Computational Linguistics
Credits: 3.0 [max 3.0]
Typically offered: Spring Odd Year
Methods/issues in computer understanding of natural language. Programming languages, their linguistic applications. Lab projects. prereq: [4201 or 5201] or programming experience or instr consent
ME 5228 - Introduction to Finite Element Modeling, Analysis, and Design
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Finite elements as principal analysis tool in computer-aided design (CAD); theoretical issues and implementation aspects for modeling and analyzing engineering problems encompassing stress analysis, heat transfer, and flow problems for linear situations. One-, two-, and three-dimensional practical engineering applications. prereq: CSE upper div or grad, 3221, AEM 3031, CSci 1113, MatS 2001
ME 5286 - Robotics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
The course deals with two major components: robot manipulators (more commonly known as the robot arm) and image processing. Lecture topics covered under robot manipulators include their forward and inverse kinematics, the mathematics of homogeneous transformations and coordinate frames, the Jacobian and velocity control, task programming, computational issues related to robot control, determining path trajectories, reaction forces, manipulator dynamics and control. Topics under computer vision include: image sensors, digitization, preprocessing, thresholding, edge detection, segmentation, feature extraction, and classification techniques. A weekly 2 hr. laboratory lasting for 8-9 weeks, will provide students with practical experience using and programming robots; students will work in pairs and perform a series of experiments using a collaborative robot. prereq: [3281 or equiv], [upper div ME or AEM or CSci or grad student]
MICE 5035 - Personal Microbiome Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Personal Microbiome Analysis, an introduction to the computational exploration and analysis of your inner microbial community, also known as your microbiome. In this course, you will have the opportunity to explore your own microbiome using visualization and analysis tools. Sequencing your own microbiome is encouraged but not required for the course. Introductory biology or genetics is recommended: BIOL 1009, GCD 3022 or BIOL 4003.
PHYS 4041 - Computational Methods in the Physical Sciences
Credits: 4.0 [max 4.0]
Course Equivalencies: 01838 - Ast 4041/Phys 4041
Typically offered: Periodic Fall & Spring
Introduction to using computer programs to solve problems in physical sciences. Selected numerical methods, mapping problems onto computational algorithms. Arranged lab. prereq: Upper div or grad student or instr consent
PHYS 4051 - Methods of Experimental Physics I
Credits: 5.0 [max 5.0]
Typically offered: Every Fall
Contemporary experimental techniques. Introduction to modern analog and digital electronics from an experimental viewpoint. Use of computers for data acquisition and experimental control. Statistics of data analysis. prereq: concurrent registration is required (or allowed) in 2605 or equiv lab experience or instr consent
PSY 5018H - Mathematical Models of Human Behavior
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Periodic Fall
Mathematical models of complex human behavior, including individual/group decision making, information processing, learning, perception, and overt action. Specific computational techniques drawn from decision theory, information theory, probability theory, machine learning, and elements of data analysis. prereq: Math 1271 or instr consent
PSY 5038W - Introduction to Neural Networks (WI)
Credits: 3.0 [max 3.0]
Typically offered: Fall Odd Year
Parallel distributed processing models in neural/cognitive science. Linear models, Hebbian rules, self-organization, non-linear networks, optimization, representation of information. Applications to sensory processing, perception, learning, memory. prereq: [[3061 or NSC 3102], [MATH 1282 or 2243]] or instr consent
GDES 4371 - Data Visualization Studio
Credits: 3.0 [max 3.0]
Course Equivalencies: 02385 - GDes 4371/GDes 5371
Grading Basis: A-F only
Typically offered: Every Fall
Visual articulation of data. Expansive research, meticulous gathering of data, analysis. Develop cohesive graphical narratives/build solid foundation in craft of presenting data. prereq: 2345, [Design minors required to take 2345], or graduate student, or instructor's consent
GDES 5341 - Interaction Design
Credits: 3.0 [max 3.0]
Course Equivalencies: 01108
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 5372 - Data Visualization for Interactive Platforms
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
Skills/tools necessary to process large quantities of information/present them through interactive mediums. Create data visualizations for web utilizing Javascript libraries. Linear/non-linear data-driven narratives.
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
PDES 5704 - Computer-Aided Design Methods
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
This class provides an overview of how to make high-quality digital computer-based models of existing and conceptual products and interactions. Students will learn Adobe Photoshop, Adobe Illustrator, and Axure for two-dimensional design and digital prototyping. Students will also learn SolidWorks and KeyShot for three-dimensional solid modeling and rendering. prereq: Senior or grad student
CSCI 3081W - Program Design and Development (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00728 - CSci 3081W/CSci 4018W/CSci4089
Typically offered: Every Fall & Spring
Principles of programming design/analysis. Concepts in software development. Uses C/C++ language to illustrate key ideas in program design/development, data structures, debugging, files, I/O, state machines, testing, coding standards. prereq: [2021, 2041]; CS upper div, CS grad, or dept. permission
CSCI 3921W - Social, Legal, and Ethical Issues in Computing (CIV, WI)
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Impact of computers on society. Computer science perspective of ethical, legal, social, philosophical, political, and economic aspects of computing. prereq: At least soph or instr consent
CSCI 4271W - Development of Secure Software Systems (WI)
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Prerequisite of CSCI 3081W. Overview of threat modeling and security assessment in the design and development of software systems. Techniques to identify, exploit, detect, mitigate and prevent software vulnerabilities at the design, coding, application, compiler, operating system, and networking layers. Methods for effectively communicating system designs and vulnerabilities.
CSCI 4511W - Introduction to Artificial Intelligence (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 01666 - CSci 4511W/CSci 5511
Prerequisites: 2041 or #
Typically offered: Every 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 4970W - Advanced Project Laboratory (WI)
Credits: 3.0 [max 9.0]
Typically offered: Every Fall & Spring
Formulate and solve open-ended project: design, implement, interface, document, test. Team work strongly encouraged. Arranged with CSci faculty. prereq: Upper div CSci, 4061, instr consent; cannot be taken for grad cr
CSCI 5127W - Embodied Computing: Design & Prototyping (WI)
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
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.