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 2021
  • 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. The B.S. more upper division credits in computer science and related areas allowing the student to pursue more deeply a particular area of computer science and tailor the degree to a specific area of interest. For students who are more likely to practice in an area that is highly specialized or technical, the B.S. may provide a better background than our B.A. in Computer Science.
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
Calculus I
MATH 1271 - Calculus I [MATH] (4.0 cr)
or MATH 1371 - CSE Calculus I [MATH] (4.0 cr)
or MATH 1571H - Honors Calculus I [MATH] (4.0 cr)
Calculus II
MATH 1272 - Calculus II (4.0 cr)
or MATH 1372 - CSE Calculus II (4.0 cr)
or MATH 1572H - Honors Calculus II (4.0 cr)
Additional Math
Discrete Structures of Computer Science
CSCI 2011 - Discrete Structures of Computer Science (4.0 cr)
or CSCI 2011H - Honors Discrete Structures of Computer Science (4.0 cr)
or Acceptable Substitution Combination
Students pursuing this substitution option will need to contact the departmental advisors (csciug@umn.edu) after their grade posts for MATH 4707 so an exception can be made to count the course towards their upper division track.
MATH 2283 - Sequences, Series, and Foundations (3.0 cr)
or MATH 3283W - Sequences, Series, and Foundations: Writing Intensive [WI] (4.0 cr)
MATH 4707 - Introduction to Combinatorics and Graph Theory (4.0 cr)
Required prerequisites
Computer Science Introductory Core
Computer Science Foundation Courses
Option 1 (Preferred)
Subgroup 0
CSCI 1133 - Introduction to Computing and Programming Concepts (4.0 cr)
or CSCI 1133H - Honors Introduction to Computing and Programming Concepts (4.0 cr)
Subgroup 1
CSCI 1933 - Introduction to Algorithms and Data Structures (4.0 cr)
or CSCI 1933H - Honors 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. At least 19 upper-division credits in the major must be taken at the University of Minnesota Twin Cities campus.
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)
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)
· 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 (4.0 cr)
or Acceptable Substitutions with MATH 4242
Students pursuing this substitution option will need to contact the departmental advisors (csciug@umn.edu) after their grade posts for MATH 4242 so an exception can be made to count the course towards their upper division track.
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 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 - Theory of Statistics II (4.0 cr)
or STAT 8101 - Theory of Statistics 1 (3.0 cr)
or STAT 8102 - Theory of Statistics 2 (3.0 cr)
or MATH 4653 - Elementary Probability (4.0 cr)
or MATH 5651 - Basic Theory of Probability and Statistics (4.0 cr)
or STAT 3011 - Introduction to Statistical Analysis [MATH] (4.0 cr)
STAT 3022 - Data Analysis (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
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 4151 - Elementary Set Theory (3.0 cr)
· MATH 4242 - Applied Linear Algebra (4.0 cr)
· MATH 4281 - Introduction to Modern Algebra (4.0 cr)
· MATH 4428 - Mathematical Modeling (4.0 cr)
· MATH 4512 - Differential Equations with Applications (3.0 cr)
· MATH 4567 - Applied Fourier Analysis (4.0 cr)
· MATH 4603 - Advanced Calculus I (4.0 cr)
· MATH 4604 - Advanced Calculus II (4.0 cr)
· MATH 4653 - Elementary Probability (4.0 cr)
· MATH 5248 - Cryptology and Number Theory (4.0 cr)
· MATH 5251 - Error-Correcting Codes, Finite Fields, Algebraic Curves (4.0 cr)
· MATH 5285H - Honors: Fundamental Structures of Algebra I (4.0 cr)
· MATH 5286H - Honors: Fundamental Structures of Algebra II (4.0 cr)
· MATH 5335 - Geometry I (4.0 cr)
· MATH 5345H - Honors: Introduction to Topology (4.0 cr)
· MATH 5378 - Differential Geometry (4.0 cr)
· MATH 5385 - Introduction to Computational Algebraic Geometry (4.0 cr)
· MATH 5445 - Mathematical Analysis of Biological Networks (4.0 cr)
· MATH 5447 - Theoretical Neuroscience (4.0 cr)
· MATH 5467 - Introduction to the Mathematics of Image and Data Analysis (4.0 cr)
· MATH 5485 - Introduction to Numerical Methods I (4.0 cr)
· MATH 5486 - Introduction To Numerical Methods II (4.0 cr)
· MATH 5525 - Introduction to Ordinary Differential Equations (4.0 cr)
· MATH 5535 - Dynamical Systems and Chaos (4.0 cr)
· MATH 5583 - Complex Analysis (4.0 cr)
· MATH 5587 - Elementary Partial Differential Equations I (4.0 cr)
· MATH 5588 - Elementary Partial Differential Equations II (4.0 cr)
· MATH 5615H - Honors: Introduction to Analysis I (4.0 cr)
· MATH 5616H - Honors: Introduction to Analysis II (4.0 cr)
· MATH 5652 - Introduction to Stochastic Processes (4.0 cr)
· MATH 5654 - Prediction and Filtering (4.0 cr)
· MATH 5705 - Enumerative Combinatorics (4.0 cr)
· MATH 5711 - Linear Programming and Combinatorial Optimization (4.0 cr)
· MATH 4152 - Elementary Mathematical Logic (3.0 cr)
or MATH 5165 - Mathematical Logic I (4.0 cr)
· MATH 5651 - Basic Theory of Probability and Statistics (4.0 cr)
or STAT 5101 - Theory of Statistics I (4.0 cr)
· MATH 4707 - Introduction to Combinatorics and Graph Theory (4.0 cr)
or MATH 5707 - Graph Theory and Non-enumerative Combinatorics (4.0 cr)
· Computer Science Electives
Take 0 - 22 credit(s) from the following:
· 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)
· CSCI 4011 - Formal Languages and Automata Theory (4.0 cr)
· CSCI 4131 - Internet Programming (3.0 cr)
· CSCI 4271W - Development of Secure Software Systems [WI] (4.0 cr)
· CSCI 4611 - Programming Interactive Computer Graphics and Games (3.0 cr)
· CSCI 4950 - Senior Software Project (3.0 cr)
· CSCI 5103 - Operating Systems (3.0 cr)
· CSCI 5105 - Introduction to Distributed Systems (3.0 cr)
· CSCI 5106 - Programming Languages (3.0 cr)
· CSCI 5115 - User Interface Design, Implementation and Evaluation (3.0 cr)
· CSCI 5117 - Developing the Interactive Web (3.0 cr)
· CSCI 5123 - Recommender Systems (3.0 cr)
· CSCI 5125 - Collaborative and Social Computing (3.0 cr)
· CSCI 5127W - Embodied Computing: Design & Prototyping [WI] (3.0 cr)
· CSCI 5143 - Real-Time and Embedded Systems (3.0 cr)
· CSCI 5161 - Introduction to Compilers (3.0 cr)
· CSCI 5221 - Foundations of Advanced Networking (3.0 cr)
· CSCI 5231 - Wireless and Sensor Networks (3.0 cr)
· CSCI 5271 - Introduction to Computer Security (3.0 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 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr)
· CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics (3.0 cr)
· CSCI 5471 - Modern Cryptography (3.0 cr)
· CSCI 5481 - Computational Techniques for Genomics (3.0 cr)
· CSCI 5512 - Artificial Intelligence II (3.0 cr)
· CSCI 5521 - Introduction to Machine Learning (3.0 cr)
· CSCI 5523 - Introduction to Data Mining (3.0 cr)
· CSCI 5525 - Machine Learning (3.0 cr)
· CSCI 5551 - Introduction to Intelligent Robotic Systems (3.0 cr)
· CSCI 5552 - Sensing and Estimation in Robotics (3.0 cr)
· CSCI 5561 - Computer Vision (3.0 cr)
· CSCI 5563 - Multiview 3D Geometry in Computer Vision (3.0 cr)
· CSCI 5607 - Fundamentals of Computer Graphics 1 (3.0 cr)
· CSCI 5608 - Fundamentals of Computer Graphics II (3.0 cr)
· CSCI 5609 - Visualization (3.0 cr)
· CSCI 5611 - Animation & Planning in Games (3.0 cr)
· CSCI 5619 - Virtual Reality and 3D Interaction (3.0 cr)
· CSCI 5708 - Architecture and Implementation of Database Management Systems (3.0 cr)
· CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science (3.0 cr)
· CSCI 5751 - Big Data Engineering and Architecture (3.0 cr)
· CSCI 5801 - Software Engineering I (3.0 cr)
· CSCI 5802 - Software Engineering II (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 4541 - Digital Signal Processing (3.0 cr)
· EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)
· EE 5251 - Optimal Filtering and Estimation (3.0 cr)
· EE 5351 - Applied Parallel Programming (3.0 cr)
· EE 5355 - Algorithmic Techniques for Scalable Many-core Computing (3.0 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)
· HINF 5610 - Foundations of Biomedical Natural Language Processing (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 3011 - Optimization I (4.0 cr)
· IE 3012 - Optimization II (4.0 cr)
· IE 3013 - Optimization for Machine Learning (4.0 cr)
· IE 4011 - Stochastic Models (4.0 cr)
· IE 5531 - Engineering Optimization I (4.0 cr)
· IE 5533 - Operations Research for Data Science (3.0 cr)
· IE 5545 - Decision Analysis (4.0 cr)
· IE 5561 - Analytics and Data-Driven Decision Making (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 4151 - Elementary Set Theory (3.0 cr)
· MATH 4242 - Applied Linear Algebra (4.0 cr)
· MATH 4281 - Introduction to Modern Algebra (4.0 cr)
· MATH 4428 - Mathematical Modeling (4.0 cr)
· MATH 4512 - Differential Equations with Applications (3.0 cr)
· MATH 4567 - Applied Fourier Analysis (4.0 cr)
· MATH 4603 - Advanced Calculus I (4.0 cr)
· MATH 4604 - Advanced Calculus II (4.0 cr)
· MATH 4653 - Elementary Probability (4.0 cr)
· MATH 5075 - Mathematics of Options, Futures, and Derivative Securities I (4.0 cr)
· MATH 5076 - Mathematics of Options, Futures, and Derivative Securities II (4.0 cr)
· MATH 5248 - Cryptology and Number Theory (4.0 cr)
· MATH 5251 - Error-Correcting Codes, Finite Fields, Algebraic Curves (4.0 cr)
· MATH 5285H - Honors: Fundamental Structures of Algebra I (4.0 cr)
· MATH 5286H - Honors: Fundamental Structures of Algebra II (4.0 cr)
· MATH 5335 - Geometry I (4.0 cr)
· MATH 5345H - Honors: Introduction to Topology (4.0 cr)
· MATH 5378 - Differential Geometry (4.0 cr)
· MATH 5385 - Introduction to Computational Algebraic Geometry (4.0 cr)
· MATH 5445 - Mathematical Analysis of Biological Networks (4.0 cr)
· MATH 5447 - Theoretical Neuroscience (4.0 cr)
· MATH 5467 - Introduction to the Mathematics of Image and Data Analysis (4.0 cr)
· MATH 5485 - Introduction to Numerical Methods I (4.0 cr)
· MATH 5486 - Introduction To Numerical Methods II (4.0 cr)
· MATH 5525 - Introduction to Ordinary Differential Equations (4.0 cr)
· MATH 5535 - Dynamical Systems and Chaos (4.0 cr)
· MATH 5583 - Complex Analysis (4.0 cr)
· MATH 5587 - Elementary Partial Differential Equations I (4.0 cr)
· MATH 5588 - Elementary Partial Differential Equations II (4.0 cr)
· MATH 5615H - Honors: Introduction to Analysis I (4.0 cr)
· MATH 5616H - Honors: Introduction to Analysis II (4.0 cr)
· MATH 5652 - Introduction to Stochastic Processes (4.0 cr)
· MATH 5654 - Prediction and Filtering (4.0 cr)
· MATH 5705 - Enumerative Combinatorics (4.0 cr)
· MATH 5711 - Linear Programming and Combinatorial Optimization (4.0 cr)
· 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 3301 - Regression and Statistical Computing (4.0 cr)
· STAT 4051 - Applied Statistics I (4.0 cr)
· STAT 4052 - Introduction to Statistical Learning (4.0 cr)
· STAT 4101 - Theory of Statistics I (4.0 cr)
· STAT 5201 - Sampling Methodology in Finite Populations (3.0 cr)
· STAT 5302 - Applied Regression Analysis (4.0 cr)
· STAT 5303 - Designing Experiments (4.0 cr)
· STAT 5401 - Applied Multivariate Methods (3.0 cr)
· STAT 5421 - Analysis of Categorical Data (3.0 cr)
· STAT 5511 - Time Series Analysis (3.0 cr)
· STAT 5601 - Nonparametric Methods (3.0 cr)
· STAT 5701 - Statistical Computing (3.0 cr)
· CSCI 4203 - Computer Architecture (4.0 cr)
or EE 4363 - Computer Architecture and Machine Organization (4.0 cr)
· CSCI 4211 - Introduction to Computer Networks (3.0 cr)
or CSCI 5211 - Data Communications and Computer Networks (3.0 cr)
· CSCI 4511W - Introduction to Artificial Intelligence [WI] (4.0 cr)
or CSCI 5511 - Artificial Intelligence I (3.0 cr)
· CSCI 4707 - Practice of Database Systems (3.0 cr)
or CSCI 5707 - Principles of Database Systems (3.0 cr)
· CSCI 4921 - History of Computing [TS, HIS] (3.0 cr)
or HSCI 4321 - History of Computing [TS, HIS] (3.0 cr)
· CSCI 5204 - Advanced Computer Architecture (3.0 cr)
or EE 5364 - Advanced Computer Architecture (3.0 cr)
· MATH 4152 - Elementary Mathematical Logic (3.0 cr)
or MATH 5165 - Mathematical Logic I (4.0 cr)
· MATH 5651 - Basic Theory of Probability and Statistics (4.0 cr)
or STAT 5101 - Theory of Statistics I (4.0 cr)
· MATH 4707 - Introduction to Combinatorics and Graph Theory (4.0 cr)
or MATH 5707 - Graph Theory and Non-enumerative Combinatorics (4.0 cr)
· STAT 4102 - Theory of Statistics II (4.0 cr)
or STAT 5102 - Theory of Statistics II (4.0 cr)
· STAT 5731 - Bayesian Astrostatistics (4.0 cr)
or AST 5731 - Bayesian Astrostatistics (4.0 cr)
· Take 0 - 3 credit(s) from the following:
· CSCI 4970W - Advanced Project Laboratory [WI] (3.0 cr)
· CSCI 5980 - Special Topics in Computer Science (1.0-3.0 cr)
· CSCI 5991 - Independent Study (1.0-3.0 cr)
· CSCI 5994 - Directed Research (1.0-3.0 cr)
· 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 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
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 toward 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. 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-plan 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. *Please refer to the Department of Computer Science & Engineering webpage for more details on which courses count for specific breadth requirements.
 
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MATH 1271 - Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: 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 1371 - CSE Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 1271/Math 1281/Math 1371/
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 1281/Math 1371/
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 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: 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
CSCI 2011H - Honors Discrete Structures of Computer Science
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 2011/CSci 2011H
Grading Basis: A-F only
Typically offered: Every Spring
Foundations of discrete mathematics. Sets, sequences, functions, big-O, propositional/predicate logic, proof methods, counting methods, recursion/recurrences, relations, trees/graph fundamentals. Advanced topics in discrete structures as time permits. prereq: [MATH 1271 or MATH 1371 or MATH 1571H], honors student.
MATH 2283 - Sequences, Series, and Foundations
Credits: 3.0 [max 3.0]
Course Equivalencies: Math 2283/3283W
Typically offered: Every Fall & Spring
Mathematical reasoning. Elements of 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. 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-
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-
MATH 4707 - Introduction to Combinatorics and Graph Theory
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Existence, enumeration, construction, algorithms, optimization. Pigeonhole principle, bijective combinatorics, inclusion-exclusion, recursions, graph modeling, isomorphism. Degree sequences and edge counting. Connectivity, Eulerian graphs, trees, Euler's formula, network flows, matching theory. Mathematical induction as proof technique. prereq: 2243, [2283 or 3283]
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 1133H - Honors Introduction to Computing and Programming Concepts
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 1133/CSci 1133H
Grading Basis: A-F only
Typically offered: Every Fall
Programming concepts using Python language. Real world problem solving, recursion, object-oriented programming. Algorithm development techniques. Abstractions/modularity. Optional honors topics: programming robots, programming paradigms, artificial intelligence. 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], CSci majors, pre-majors in CSE/CLA, honors student
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
CSCI 1933H - Honors Introduction to Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 1902/CSci 1933/CSci 1933H
Grading Basis: A-F only
Typically offered: Every Spring
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. Optional honors topics: Advanced Java topics, GUI programming, CS research examples. prereq: [1133 or 1133H] and honors student, or inst 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: 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: 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: 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.
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: Biol 4003/GCD 3022
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: 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: 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: 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. prereq: 1401V, honors student or permission of University Honors Program
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: Chem 1061/Chem 1071H/Chem 1081
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: 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: Chem 1061/Chem 1071H/Chem 1081
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: 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: 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: 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: 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: 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: CSci 3081W/CSci 4018W/CSci4089
Typically offered: Every Fall & Spring
Principles of programming design/analysis. Concepts in software development. Uses a programming project to illustrate key ideas in program design/development, data structures, debugging, files, I/O, testing, and 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: 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, 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: 4.0 [max 1.0]
Typically offered: Every Fall & Spring
This course has three primary objectives. (1) To present the basic theory of linear algebra, including: solving systems of linear equations; determinants; the theory of Euclidean vector spaces and general vector spaces; eigenvalues and eigenvectors of matrices; inner products; diagonalization of quadratic forms; and linear transformations between vector spaces. (2) To introduce certain aspects of numerical linear algebra and computation. (3) To introduce applications of linear algebra to other domains such as data science. Objectives (2) and (3) will be taught with hands-on computer projects in a high-level programming language. Prerequisites: MATH 1272 or equivalent
MATH 4242 - Applied Linear Algebra
Credits: 4.0 [max 4.0]
Course Equivalencies: 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/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 2471 - UM Talented Youth Mathematics Program--Calculus II, Second Semester (MATH)
Credits: 2.0 [max 4.0]
Course Equivalencies: 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: 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
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 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]
Typically offered: Every Fall & 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 - 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]
STAT 8101 - Theory of Statistics 1
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Review of linear algebra. Introduction to probability theory. Random variables, their transformations/expectations. Standard distributions, including multivariate Normal distribution. Probability inequalities. Convergence concepts, including laws of large numbers, Central Limit Theorem. delta method. Sampling distributions. prereq: Statistics grad major or instr consent
STAT 8102 - Theory of Statistics 2
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference. Sufficiency. Likelihood-based methods. Point estimation. Confidence intervals. Neyman Pearson hypothesis testing theory. Introduction to theory of linear models. prereq: 8101, Statistics graduate major 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: 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.
STAT 3011 - Introduction to Statistical Analysis (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: AnSc 3011/ESPM 3012/Stat 3011/
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
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: [EE 1301 (preferred) or CSCI 1113 or CSCI 1103 or CSci 1133]
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
MATH 4151 - Elementary Set Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Basic properties of operations on sets, cardinal numbers, simply and well-ordered sets, ordinal numbers, axiom of choice, axiomatics. prereq: One soph math course or instr consent
MATH 4242 - Applied Linear Algebra
Credits: 4.0 [max 4.0]
Course Equivalencies: 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 4281 - Introduction to Modern Algebra
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall
Equivalence relations, greatest common divisor, prime decomposition, modular arithmetic, groups, rings, fields, Chinese remainder theorem, matrices over commutative rings, polynomials over fields. prereq: 2283 or 3283 or instr consent
MATH 4428 - Mathematical Modeling
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Modeling techniques for analysis/decision-making in industry. Optimization (sensitivity analysis, Lagrange multipliers, linear programming). Dynamical modeling (steady-states, stability analysis, eigenvalue methods, phase portraits, simulation). Probabilistic methods (probability/statistical models, Markov chains, linear regression, simulation). prereq: 2243 or 2373 or 2573
MATH 4512 - Differential Equations with Applications
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Laplace transforms, series solutions, systems, numerical methods, plane autonomous systems, stability. prereq: 2243 or 2373 or 2573
MATH 4567 - Applied Fourier Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Fourier series, integral/transform. Convergence. Fourier series, transform in complex form. Solution of wave, heat, Laplace equations by separation of variables. Sturm-Liouville systems, finite Fourier, fast Fourier transform. Applications. Other topics as time permits. prereq: 2243 or 2373 or 2573
MATH 4603 - Advanced Calculus I
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 4606/Math 5615/Math 5616
Typically offered: Every Fall, Spring & Summer
Axioms for the real numbers. Techniques of proof for limits, continuity, uniform convergence. Rigorous treatment of differential/integral calculus for single-variable functions. prereq: [[2243 or 2373], [2263 or 2374]] or 2574 or instr consent
MATH 4604 - Advanced Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 4604/Math 5616
Typically offered: Every Spring
Sequel to MATH 4603. Topology of n-dimensional Euclidean space. Rigorous treatment of multivariable differentiation and integration, including chain rule, Taylor's Theorem, implicit function theorem, Fubini's Theorem, change of variables, Stokes' Theorem. prereq: 4603 or 5615 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 5248 - Cryptology and Number Theory
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Classical cryptosystems. One-time pads, perfect secrecy. Public key ciphers: RSA, discrete log. Euclidean algorithm, finite fields, quadratic reciprocity. Message digest, hash functions. Protocols: key exchange, secret sharing, zero-knowledge proofs. Probablistic algorithms: pseudoprimes, prime factorization. Pseudo-random numbers. Elliptic curves. prereq: 2 sems soph math
MATH 5251 - Error-Correcting Codes, Finite Fields, Algebraic Curves
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Information theory: channel models, transmission errors. Hamming weight/distance. Linear codes/fields, check bits. Error processing: linear codes, Hamming codes, binary Golay codes. Euclidean algorithm. Finite fields, Bose-Chaudhuri-Hocquenghem codes, polynomial codes, Goppa codes, codes from algebraic curves. prereq: 2 sems soph math
MATH 5285H - Honors: Fundamental Structures of Algebra I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Review of matrix theory, linear algebra. Vector spaces, linear transformations over abstract fields. Group theory, including normal subgroups, quotient groups, homomorphisms, class equation, Sylow's theorems. Specific examples: permutation groups, symmetry groups of geometric figures, matrix groups. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283]
MATH 5286H - Honors: Fundamental Structures of Algebra II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Ring/module theory, including ideals, quotients, homomorphisms, domains (unique factorization, euclidean, principal ideal), fundamental theorem for finitely generated modules over euclidean domains, Jordan canonical form. Introduction to field theory, including finite fields, algebraic/transcendental extensions, Galois theory. prereq: 5285
MATH 5335 - Geometry I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Advanced two-dimensional Euclidean geometry from a vector viewpoint. Theorems/problems about triangles/circles, isometries, connections with Euclid's axioms. Hyperbolic geometry, how it compares with Euclidean geometry. prereq: [2243 or 2373 or 2573], [concurrent registration is required (or allowed) in 2263 or concurrent registration is required (or allowed) in 2374 or concurrent registration is required (or allowed) in 2574]
MATH 5345H - Honors: Introduction to Topology
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Rigorous introduction to general topology. Set theory, Euclidean/metric spaces, compactness/connectedness. May include Urysohn metrization, Tychonoff theorem or fundamental group/covering spaces. prereq: [2263 or 2374 or 2573], [concurrent registration is required (or allowed) in 2283 or concurrent registration is required (or allowed) in 2574 or concurrent registration is required (or allowed) in 3283]
MATH 5378 - Differential Geometry
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Basic geometry of curves in plane and in space, including Frenet formula, theory of surfaces, differential forms, Riemannian geometry. prereq: [2263 or 2374 or 2573], [2243 or 2373 or 2574]; [2283 or 3283] recommended]
MATH 5385 - Introduction to Computational Algebraic Geometry
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Geometry of curves/surfaces defined by polynomial equations. Emphasizes concrete computations with polynomials using computer packages, interplay between algebra and geometry. Abstract algebra presented as needed. prereq: [2263 or 2374 or 2573], [2243 or 2373 or 2574]
MATH 5445 - Mathematical Analysis of Biological Networks
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Development/analysis of models for complex biological networks. Examples taken from signal transduction networks, metabolic networks, gene control networks, and ecological networks. prereq: Linear algebra, differential equations
MATH 5447 - Theoretical Neuroscience
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Nonlinear dynamical system models of neurons and neuronal networks. Computation by excitatory/inhibitory networks. Neural oscillations, adaptation, bursting, synchrony. Memory systems. prereq: 2243 or 2373 or 2574
MATH 5467 - Introduction to the Mathematics of Image and Data Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Background theory/experience in wavelets. Inner product spaces, operator theory, Fourier transforms applied to Gabor transforms, multi-scale analysis, discrete wavelets, self-similarity. Computing techniques. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283 or instr consent]; [[2263 or 2374], 4567] recommended
MATH 5485 - Introduction to Numerical Methods I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Solution of nonlinear equations in one variable. Interpolation, polynomial approximation. Methods for solving linear systems, eigenvalue problems, systems of nonlinear equations. prereq: [2243 or 2373 or 2573], familiarity with some programming language
MATH 5486 - Introduction To Numerical Methods II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Numerical integration/differentiation. Numerical solution of initial-value problems, boundary value problems for ordinary differential equations, partial differential equations. prereq: 5485
MATH 5525 - Introduction to Ordinary Differential Equations
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Ordinary differential equations, solution of linear systems, qualitative/numerical methods for nonlinear systems. Linear algebra background, fundamental matrix solutions, variation of parameters, existence/uniqueness theorems, phase space. Rest points, their stability. Periodic orbits, Poincare-Bendixson theory, strange attractors. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283]
MATH 5535 - Dynamical Systems and Chaos
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Dynamical systems theory. Emphasizes iteration of one-dimensional mappings. Fixed points, periodic points, stability, bifurcations, symbolic dynamics, chaos, fractals, Julia/Mandelbrot sets. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]
MATH 5583 - Complex Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 3574/Math 5583
Typically offered: Every Fall, Spring & Summer
Algebra, geometry of complex numbers. Linear fractional transformations. Conformal mappings. Holomorphic functions. Theorems of Abel/Cauchy, power series. Schwarz' lemma. Complex exponential, trig functions. Entire functions, theorems of Liouville/Morera. Reflection principle. Singularities, Laurent series. Residues. prereq: 2 sems soph math [including [2263 or 2374 or 2573], [2283 or 3283]] recommended
MATH 5587 - Elementary Partial Differential Equations I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Emphasizes partial differential equations w/physical applications, including heat, wave, Laplace's equations. Interpretations of boundary conditions. Characteristics, Fourier series, transforms, Green's functions, images, computational methods. Applications include wave propagation, diffusions, electrostatics, shocks. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]
MATH 5588 - Elementary Partial Differential Equations II
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Heat, wave, Laplace's equations in higher dimensions. Green's functions, Fourier series, transforms. Asymptotic methods, boundary layer theory, bifurcation theory for linear/nonlinear PDEs. Variational methods. Free boundary problems. Additional topics as time permits. prereq: [[2243 or 2373 or 2573], [2263 or 2374 or 2574], 5587] or instr consent
MATH 5615H - Honors: Introduction to Analysis I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574
MATH 5616H - Honors: Introduction to Analysis II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615
MATH 5652 - Introduction to Stochastic Processes
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101
MATH 5654 - Prediction and Filtering
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Markov chains, Wiener process, stationary sequences, Ornstein-Uhlenbeck process. Partially observable Markov processes (hidden Markov models), stationary processes. Equations for general filters, Kalman filter. Prediction of future values of partially observable processes. prereq: 5651 or Stat 5101
MATH 5705 - Enumerative Combinatorics
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Basic enumeration, bijections, inclusion-exclusion, recurrence relations, ordinary/exponential generating functions, partitions, Polya theory. Optional topics include trees, asymptotics, listing algorithms, rook theory, involutions, tableaux, permutation statistics. prereq: [2243 or 2373 or 2573], [2263 or 2283 or 2374 or 2574 or 3283]
MATH 5711 - Linear Programming and Combinatorial Optimization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Simplex method, connections to geometry, duality theory, sensitivity analysis. Applications to cutting stock, allocation of resources, scheduling problems. Flows, matching/transportation problems, spanning trees, distance in graphs, integer programs, branch/bound, cutting planes, heuristics. Applications to traveling salesman, knapsack problems. prereq: 2 sems soph math [including 2243 or 2373 or 2573]
MATH 4152 - Elementary Mathematical Logic
Credits: 3.0 [max 3.0]
Course Equivalencies: Math 4152/5165
Typically offered: Every Spring
Propositional logic. Predicate logic: notion of a first order language, a deductive system for first order logic, first order structures, Godel's completeness theorem, axiom systems, models of formal theories. prereq: one soph math course or instr consent
MATH 5165 - Mathematical Logic I
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 4152/5165
Typically offered: Every Fall
Theory of computability: notion of algorithm, Turing machines, primitive recursive functions, recursive functions, Kleene normal form, recursion theorem. Propositional logic. prereq: 2283 or 3283 or Phil 5201 or CSci course in theory of algorithms or instr consent
MATH 5651 - Basic Theory of Probability and Statistics
Credits: 4.0 [max 4.0]
Course Equivalencies: 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.
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)
MATH 4707 - Introduction to Combinatorics and Graph Theory
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Existence, enumeration, construction, algorithms, optimization. Pigeonhole principle, bijective combinatorics, inclusion-exclusion, recursions, graph modeling, isomorphism. Degree sequences and edge counting. Connectivity, Eulerian graphs, trees, Euler's formula, network flows, matching theory. Mathematical induction as proof technique. prereq: 2243, [2283 or 3283]
MATH 5707 - Graph Theory and Non-enumerative Combinatorics
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Basic topics in graph theory: connectedness, Eulerian/Hamiltonian properties, trees, colorings, planar graphs, matchings, flows in networks. Optional topics include graph algorithms, Latin squares, block designs, Ramsey theory. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]; [2283 or 3283 or experience in writing proofs] highly recommended; Credit will not be granted if credit has been received for: 4707
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: 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: Biol 3272Biol 3272H//Biol 5272
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
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 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 4271W - Development of Secure Software Systems (WI)
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
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. Prerequisites: 3081w
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 4950 - Senior Software Project
Credits: 3.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Student teams develop a software system, distribute system to users, and extend/maintain it in response to their needs. Software engineering techniques. Software development, team participation, leadership. prereq: Upper div CSci, 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 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
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 5123 - Recommender Systems
Credits: 3.0 [max 3.0]
Typically offered: Fall Odd Year
An overview of recommender systems, including content-based and collaborative algorithms for recommendation, programming of recommender systems, and evaluation and metrics for recommender systems. prereq: Java programming and 2033 and 3081, or instructor consent.
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 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.
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 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 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 5231 - Wireless and Sensor Networks
Credits: 3.0 [max 3.0]
Typically offered: Spring Odd Year
Enabling technologies, including hardware, embedded operating systems, programming environment, communication, networking, and middleware services. Hands-on experience in programming tiny communication devices. prereq: 4211 or 5211 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 equiv 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 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 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 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 - Introduction to Machine Learning
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] 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 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
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: 5511 or instr consent
CSCI 5563 - Multiview 3D Geometry in Computer Vision
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
The 3D spatial relationship between cameras and scenes in computer vision. Application to tasks such as planning robots, reconstructing scenes from photos, and understanding human behaviors from body-worn cameras data. Multiview theory fundamentals, structure-from-motion, state-of-the-art approaches, and current research integration. prereq: Undergraduate students enrolling in the course must have completed CSCI 2033 or have instructor consent.
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 5608 - Fundamentals of Computer Graphics II
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Advanced topics in image synthesis, modeling, rendering. Image processing, image warping, global illumination, non-photorealistic rendering, texture synthesis. Parametric cubic surfaces, subdivision surfaces, acceleration techniques, advanced texture mapping. Programming in C/C++. prereq: 5607 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 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
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: 4707 or 5707 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 5751 - Big Data Engineering and Architecture
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Big data and data-intensive application management, design and processing concepts. Data modeling on different NoSQL databases: key/value, column-family, document, graph-based stores. Stream and real-time processing. Big data architectures. Distributed computing using Spark, Hadoop or other distributed systems. Big data projects. prereq: 4041, 5707, or instructor 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 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: 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 4541 - Digital Signal Processing
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Summer
Review of linear discrete time systems and sampled/digital signals. Fourier analysis, discrete/fast Fourier transforms. Interpolation/decimation. Design of analog, infinite-impulse response, and finite impulse response filters. Quantization effects. prereq: [3015, 3025] or instr consent
EE 5239 - Introduction to Nonlinear Optimization
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications. prereq: [3025, Math 2373, Math 2374, CSE grad student] or dept consent
EE 5251 - Optimal Filtering and Estimation
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5451/EE 5251
Typically offered: Every Fall
Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications. prereq: [[[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; 3025, 4231 recommended
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 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.
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.
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: FNRM 3131/FNRM 5131/FR 3131/
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: FNRM 3262/FNRM 5262
Typically offered: Every Fall & Spring
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: 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
HINF 5610 - Foundations of Biomedical Natural Language Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
The course will provide a systematic introduction to basic knowledge and methods used in natural language processing (NLP) research. It will introduce biomedical NLP tasks and methods as well as their resources and applications in the biomedical domain. The course will also provide hands-on experience with existing NLP tools and systems. Students will gain basic knowledge and skills in handling with main biomedical NLP tasks. Prerequisites graduate student or instructor consent; Experience with at least one programming language (Python or Perl preferred) Recommended: basic understanding of data mining concepts, basic knowledge of computational linguistics
HSCI 4321 - History of Computing (TS, HIS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 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 3011 - Optimization I
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Optimization models, data/solutions, linear programming, simplex method, duality theory, sensitivity analysis, network optimization models, integer programming. prereq: 1101, MATH 2374, MATH 2142, Upper Division CSE
IE 3012 - Optimization II
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Spring
Classifying optimization models. Modeling binary variables, branch and bound. Shortest path. Minimum spanning tree. Nonlinear programming, global and local optima, optimality conditions. Algebraic modeling languages and optimization solvers. prereq: 3011, ISyE major
IE 3013 - Optimization for Machine Learning
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Machine learning has been widely used in areas such as computer vision, search engines, speech recognition, robotics, recommendation systems, bioinformatics, social networks, and finance. It has become an important tool in prediction and data analysis. This course introduces some fundamental solution methods for solving various optimization models arising in the context of machine learning.
IE 4011 - Stochastic Models
Credits: 4.0 [max 4.0]
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 2142, MATH 2374, ISyE major
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 5533 - Operations Research for Data Science
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Periodic Fall
This course combines data, modeling, and decision-making to provide students with experience solving practical problems in a variety of application areas, including healthcare and medical decision-making, supply chains and e-commerce, and finance and revenue management. To this end, case studies will be used to illustrate the sequence of problem definition, data analysis, model building, and decision support. The example problems are realistic in terms of size and complexity and the data sets are realistic in that the quality of the data is less-than-perfect. The first part of the course focuses on deterministic models while the second part of the course covers stochastic models. A high-level programming language such as R is used for data manipulation and for predictive analytics. An algebraic modeling language such as AMPL is used for models that require linear/integer programming. The solutions and their sensitivity to changes in parameters are interpreted to aid decision-makers. Throughout the course, the methodologies are kept in perspective with the overall goal of making better decisions.
IE 5545 - Decision Analysis
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Single-person and group decision problems. Structuring of decision problems arising in personal, business, and public policy contexts. Decision-making under uncertainty, value of information, games of complete information and Nash equilibrium, Bayesian games, group decision-making and distributed consensus, basics of mechanism design. prereq: 3521 or equiv
IE 5561 - Analytics and Data-Driven Decision Making
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Hands-on experience with modern methods for analytics and data-driven decision making. Methodologies such as linear and integer optimization and supervised and unsupervised learning will be brought together to address problems in a variety of areas such as healthcare, agriculture, sports, energy, and finance. Students will learn how to manipulate data, build and solve models, and interpret and visualize results using a high-level, dynamic programming language. Prerequisites: IE 3521 or equivalent; IE 3011 or IE 5531 or equivalent; proficiency with a programming language such as R, Python, or C.
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
MATH 4151 - Elementary Set Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Basic properties of operations on sets, cardinal numbers, simply and well-ordered sets, ordinal numbers, axiom of choice, axiomatics. prereq: One soph math course or instr consent
MATH 4242 - Applied Linear Algebra
Credits: 4.0 [max 4.0]
Course Equivalencies: 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 4281 - Introduction to Modern Algebra
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall
Equivalence relations, greatest common divisor, prime decomposition, modular arithmetic, groups, rings, fields, Chinese remainder theorem, matrices over commutative rings, polynomials over fields. prereq: 2283 or 3283 or instr consent
MATH 4428 - Mathematical Modeling
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Modeling techniques for analysis/decision-making in industry. Optimization (sensitivity analysis, Lagrange multipliers, linear programming). Dynamical modeling (steady-states, stability analysis, eigenvalue methods, phase portraits, simulation). Probabilistic methods (probability/statistical models, Markov chains, linear regression, simulation). prereq: 2243 or 2373 or 2573
MATH 4512 - Differential Equations with Applications
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Laplace transforms, series solutions, systems, numerical methods, plane autonomous systems, stability. prereq: 2243 or 2373 or 2573
MATH 4567 - Applied Fourier Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Fourier series, integral/transform. Convergence. Fourier series, transform in complex form. Solution of wave, heat, Laplace equations by separation of variables. Sturm-Liouville systems, finite Fourier, fast Fourier transform. Applications. Other topics as time permits. prereq: 2243 or 2373 or 2573
MATH 4603 - Advanced Calculus I
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 4606/Math 5615/Math 5616
Typically offered: Every Fall, Spring & Summer
Axioms for the real numbers. Techniques of proof for limits, continuity, uniform convergence. Rigorous treatment of differential/integral calculus for single-variable functions. prereq: [[2243 or 2373], [2263 or 2374]] or 2574 or instr consent
MATH 4604 - Advanced Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 4604/Math 5616
Typically offered: Every Spring
Sequel to MATH 4603. Topology of n-dimensional Euclidean space. Rigorous treatment of multivariable differentiation and integration, including chain rule, Taylor's Theorem, implicit function theorem, Fubini's Theorem, change of variables, Stokes' Theorem. prereq: 4603 or 5615 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 5075 - Mathematics of Options, Futures, and Derivative Securities I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Mathematical background (e.g., partial differential equations, Fourier series, computational methods, Black-Scholes theory, numerical methods--including Monte Carlo simulation). Interest-rate derivative securities, exotic options, risk theory. First course of two-course sequence. prereq: Two yrs calculus, basic computer skills
MATH 5076 - Mathematics of Options, Futures, and Derivative Securities II
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Mathematical background such as partial differential equations, Fourier series, computational methods, Black-Scholes theory, numerical methods (including Monte Carlo simulation), interest-rate derivative securities, exotic options, risk theory. prereq: 5075
MATH 5248 - Cryptology and Number Theory
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Classical cryptosystems. One-time pads, perfect secrecy. Public key ciphers: RSA, discrete log. Euclidean algorithm, finite fields, quadratic reciprocity. Message digest, hash functions. Protocols: key exchange, secret sharing, zero-knowledge proofs. Probablistic algorithms: pseudoprimes, prime factorization. Pseudo-random numbers. Elliptic curves. prereq: 2 sems soph math
MATH 5251 - Error-Correcting Codes, Finite Fields, Algebraic Curves
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Information theory: channel models, transmission errors. Hamming weight/distance. Linear codes/fields, check bits. Error processing: linear codes, Hamming codes, binary Golay codes. Euclidean algorithm. Finite fields, Bose-Chaudhuri-Hocquenghem codes, polynomial codes, Goppa codes, codes from algebraic curves. prereq: 2 sems soph math
MATH 5285H - Honors: Fundamental Structures of Algebra I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Review of matrix theory, linear algebra. Vector spaces, linear transformations over abstract fields. Group theory, including normal subgroups, quotient groups, homomorphisms, class equation, Sylow's theorems. Specific examples: permutation groups, symmetry groups of geometric figures, matrix groups. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283]
MATH 5286H - Honors: Fundamental Structures of Algebra II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Ring/module theory, including ideals, quotients, homomorphisms, domains (unique factorization, euclidean, principal ideal), fundamental theorem for finitely generated modules over euclidean domains, Jordan canonical form. Introduction to field theory, including finite fields, algebraic/transcendental extensions, Galois theory. prereq: 5285
MATH 5335 - Geometry I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Advanced two-dimensional Euclidean geometry from a vector viewpoint. Theorems/problems about triangles/circles, isometries, connections with Euclid's axioms. Hyperbolic geometry, how it compares with Euclidean geometry. prereq: [2243 or 2373 or 2573], [concurrent registration is required (or allowed) in 2263 or concurrent registration is required (or allowed) in 2374 or concurrent registration is required (or allowed) in 2574]
MATH 5345H - Honors: Introduction to Topology
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Rigorous introduction to general topology. Set theory, Euclidean/metric spaces, compactness/connectedness. May include Urysohn metrization, Tychonoff theorem or fundamental group/covering spaces. prereq: [2263 or 2374 or 2573], [concurrent registration is required (or allowed) in 2283 or concurrent registration is required (or allowed) in 2574 or concurrent registration is required (or allowed) in 3283]
MATH 5378 - Differential Geometry
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Basic geometry of curves in plane and in space, including Frenet formula, theory of surfaces, differential forms, Riemannian geometry. prereq: [2263 or 2374 or 2573], [2243 or 2373 or 2574]; [2283 or 3283] recommended]
MATH 5385 - Introduction to Computational Algebraic Geometry
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Geometry of curves/surfaces defined by polynomial equations. Emphasizes concrete computations with polynomials using computer packages, interplay between algebra and geometry. Abstract algebra presented as needed. prereq: [2263 or 2374 or 2573], [2243 or 2373 or 2574]
MATH 5445 - Mathematical Analysis of Biological Networks
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Development/analysis of models for complex biological networks. Examples taken from signal transduction networks, metabolic networks, gene control networks, and ecological networks. prereq: Linear algebra, differential equations
MATH 5447 - Theoretical Neuroscience
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Nonlinear dynamical system models of neurons and neuronal networks. Computation by excitatory/inhibitory networks. Neural oscillations, adaptation, bursting, synchrony. Memory systems. prereq: 2243 or 2373 or 2574
MATH 5467 - Introduction to the Mathematics of Image and Data Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Background theory/experience in wavelets. Inner product spaces, operator theory, Fourier transforms applied to Gabor transforms, multi-scale analysis, discrete wavelets, self-similarity. Computing techniques. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283 or instr consent]; [[2263 or 2374], 4567] recommended
MATH 5485 - Introduction to Numerical Methods I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Solution of nonlinear equations in one variable. Interpolation, polynomial approximation. Methods for solving linear systems, eigenvalue problems, systems of nonlinear equations. prereq: [2243 or 2373 or 2573], familiarity with some programming language
MATH 5486 - Introduction To Numerical Methods II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Numerical integration/differentiation. Numerical solution of initial-value problems, boundary value problems for ordinary differential equations, partial differential equations. prereq: 5485
MATH 5525 - Introduction to Ordinary Differential Equations
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Ordinary differential equations, solution of linear systems, qualitative/numerical methods for nonlinear systems. Linear algebra background, fundamental matrix solutions, variation of parameters, existence/uniqueness theorems, phase space. Rest points, their stability. Periodic orbits, Poincare-Bendixson theory, strange attractors. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283]
MATH 5535 - Dynamical Systems and Chaos
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Dynamical systems theory. Emphasizes iteration of one-dimensional mappings. Fixed points, periodic points, stability, bifurcations, symbolic dynamics, chaos, fractals, Julia/Mandelbrot sets. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]
MATH 5583 - Complex Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 3574/Math 5583
Typically offered: Every Fall, Spring & Summer
Algebra, geometry of complex numbers. Linear fractional transformations. Conformal mappings. Holomorphic functions. Theorems of Abel/Cauchy, power series. Schwarz' lemma. Complex exponential, trig functions. Entire functions, theorems of Liouville/Morera. Reflection principle. Singularities, Laurent series. Residues. prereq: 2 sems soph math [including [2263 or 2374 or 2573], [2283 or 3283]] recommended
MATH 5587 - Elementary Partial Differential Equations I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Emphasizes partial differential equations w/physical applications, including heat, wave, Laplace's equations. Interpretations of boundary conditions. Characteristics, Fourier series, transforms, Green's functions, images, computational methods. Applications include wave propagation, diffusions, electrostatics, shocks. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]
MATH 5588 - Elementary Partial Differential Equations II
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Heat, wave, Laplace's equations in higher dimensions. Green's functions, Fourier series, transforms. Asymptotic methods, boundary layer theory, bifurcation theory for linear/nonlinear PDEs. Variational methods. Free boundary problems. Additional topics as time permits. prereq: [[2243 or 2373 or 2573], [2263 or 2374 or 2574], 5587] or instr consent
MATH 5615H - Honors: Introduction to Analysis I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574
MATH 5616H - Honors: Introduction to Analysis II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615
MATH 5652 - Introduction to Stochastic Processes
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101
MATH 5654 - Prediction and Filtering
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Markov chains, Wiener process, stationary sequences, Ornstein-Uhlenbeck process. Partially observable Markov processes (hidden Markov models), stationary processes. Equations for general filters, Kalman filter. Prediction of future values of partially observable processes. prereq: 5651 or Stat 5101
MATH 5705 - Enumerative Combinatorics
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Basic enumeration, bijections, inclusion-exclusion, recurrence relations, ordinary/exponential generating functions, partitions, Polya theory. Optional topics include trees, asymptotics, listing algorithms, rook theory, involutions, tableaux, permutation statistics. prereq: [2243 or 2373 or 2573], [2263 or 2283 or 2374 or 2574 or 3283]
MATH 5711 - Linear Programming and Combinatorial Optimization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Simplex method, connections to geometry, duality theory, sensitivity analysis. Applications to cutting stock, allocation of resources, scheduling problems. Flows, matching/transportation problems, spanning trees, distance in graphs, integer programs, branch/bound, cutting planes, heuristics. Applications to traveling salesman, knapsack problems. prereq: 2 sems soph math [including 2243 or 2373 or 2573]
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: 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 & Spring
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: PHYS 1302W, concurrent registration is required (or allowed) in PHYS 3605W 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
STAT 3301 - Regression and Statistical Computing
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
This is a second course in statistics for students that have completed a calculus-based introductory course. Students will learn to analyze data with the multiple linear regression model. This will include inference, diagnostics, validation, transformations, and model selection. Students will also design and perform Monte Carlo simulation studies to improve their understanding of statistical concepts like coverage probability, Type I error probability, and power. This will allow students to understand the impacts of model misspecification and the quality of approximate inference. prereqs: STAT 3021 and (CSCI 1113 or CSCI 1133)
STAT 4051 - Applied Statistics I
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
This is the first semester of the Applied Statistics sequence for majors seeking a BA or BS in statistics. The course introduces a wide variety of applied statistical methods, methodology for identifying types of problems and selecting appropriate methods for data analysis, to correctly interpret results, and to provide hands-on experience with real-life data analysis. The course covers basic concepts of single factor analysis of variance (ANOVA) with fixed and random effects, factorial designs, analysis of covariance (ANCOVA), repeated measures analysis with mixed effect models, principal component analysis (PCA) and multidimensional scaling, robust estimation and regression methods, and rank tests. Numerous datasets will be analyzed and interpreted, using the open-source statistical software R and Rstudio. prerequisites: (STAT 3701 or STAT 3301) and (STAT 4101 or STAT 5101 or MATH 5651)
STAT 4052 - Introduction to Statistical Learning
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
This is the second semester of the core Applied Statistics sequence for majors seeking a BA or BS in statistics. Both Stat 4051 and Stat 4052 are required in the major. The course introduces a wide variety of applied statistical methods, methodology for identifying types of problems and selecting appropriate methods for data analysis, to correctly interpret results, and to provide hands-on experience with real-life data analysis. The course covers basic concepts of classification, both classical methods of linear classification rules as well as modern computer-intensive methods of classification trees, and the estimation of classification errors by splitting data into training and validation data sets; non-linear parametric regression; nonparametric regression including kernel estimates; categorical data analysis; logistic and Poisson regression; and adjustments for missing data. Numerous datasets will be analyzed and interpreted, using the open-source statistical software R and Rstudio. prerequisites: STAT 4051 and (STAT 4102 or STAT 5102)
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 5201 - Sampling Methodology in Finite Populations
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Simple random, systematic, stratified, unequal probability sampling. Ratio, model based estimation. Single stage, multistage, adaptive cluster sampling. Spatial sampling. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent
STAT 5302 - Applied Regression Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.
STAT 5303 - Designing Experiments
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Analysis of variance. Multiple comparisons. Variance-stabilizing transformations. Contrasts. Construction/analysis of complete/incomplete block designs. Fractional factorial designs. Confounding split plots. Response surface design. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.
STAT 5421 - Analysis of Categorical Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/loglinear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models. prereq: STAT 3022 or 3032 or 3301 or 5302 or 4051 or 8051 or 5102 or 4102
STAT 5511 - Time Series Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102
STAT 5601 - Nonparametric Methods
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: Stat classes 3032 or 3022 or 4102 or 5021 or 5102 or instr consent
STAT 5701 - Statistical Computing
Credits: 3.0 [max 3.0]
Prerequisites: (Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent
Grading Basis: A-F or Aud
Typically offered: Every Fall
Statistical programming, function writing, graphics using high-level statistical computing languages. Data management, parallel computing, version control, simulation studies, power calculations. Using optimization to fit statistical models. Monte Carlo methods, reproducible research. prereq: (Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent
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 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 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 4511W - Introduction to Artificial Intelligence (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 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 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 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 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 4921 - History of Computing (TS, HIS)
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4921/HSci 4321
Typically offered: Fall Even Year
Developments in last 150 years; evolution of hardware and software; growth of computer and semiconductor industries and their relation to other businesses; changing relationships resulting from new data-gathering and analysis techniques; automation; social and ethical issues.
HSCI 4321 - History of Computing (TS, HIS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 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.
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 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
MATH 4152 - Elementary Mathematical Logic
Credits: 3.0 [max 3.0]
Course Equivalencies: Math 4152/5165
Typically offered: Every Spring
Propositional logic. Predicate logic: notion of a first order language, a deductive system for first order logic, first order structures, Godel's completeness theorem, axiom systems, models of formal theories. prereq: one soph math course or instr consent
MATH 5165 - Mathematical Logic I
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 4152/5165
Typically offered: Every Fall
Theory of computability: notion of algorithm, Turing machines, primitive recursive functions, recursive functions, Kleene normal form, recursion theorem. Propositional logic. prereq: 2283 or 3283 or Phil 5201 or CSci course in theory of algorithms or instr consent
MATH 5651 - Basic Theory of Probability and Statistics
Credits: 4.0 [max 4.0]
Course Equivalencies: 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.
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)
MATH 4707 - Introduction to Combinatorics and Graph Theory
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Existence, enumeration, construction, algorithms, optimization. Pigeonhole principle, bijective combinatorics, inclusion-exclusion, recursions, graph modeling, isomorphism. Degree sequences and edge counting. Connectivity, Eulerian graphs, trees, Euler's formula, network flows, matching theory. Mathematical induction as proof technique. prereq: 2243, [2283 or 3283]
MATH 5707 - Graph Theory and Non-enumerative Combinatorics
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Basic topics in graph theory: connectedness, Eulerian/Hamiltonian properties, trees, colorings, planar graphs, matchings, flows in networks. Optional topics include graph algorithms, Latin squares, block designs, Ramsey theory. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]; [2283 or 3283 or experience in writing proofs] highly recommended; Credit will not be granted if credit has been received for: 4707
STAT 4102 - Theory of Statistics II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Estimation. Significance tests. Distribution free methods. Power. Application to regression and to analysis of variance/count data. prereq: STAT 4101
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]
STAT 5731 - Bayesian Astrostatistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5731/Stat 5731
Grading Basis: A-F only
Typically offered: Every Fall
This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets and a focus on modern statistical software, such as R and python. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent Suggested: statistical course at the level of AST 4031, AST 5031, STAT 3021, or STAT 5021
AST 5731 - Bayesian Astrostatistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5731/Stat 5731
Grading Basis: A-F only
Typically offered: Every Fall
This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets and a focus on modern statistical software, such as R and python. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent Suggested: statistical course at the level of AST 4031, AST 5031, STAT 3021, or STAT 5021
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 5980 - Special Topics in Computer Science
Credits: 1.0 -3.0 [max 27.0]
Typically offered: Periodic Fall & Spring
Lectures and informal discussions on current topics in computer science. prereq: instr consent; may be repeated for cr
CSCI 5991 - Independent Study
Credits: 1.0 -3.0 [max 9.0]
Typically offered: Every Fall, Spring & Summer
Independent study arranged with CS faculty member. prereq: instr consent; may be repeated for cr
CSCI 5994 - Directed Research
Credits: 1.0 -3.0 [max 9.0]
Typically offered: Every Fall, Spring & Summer
Directed research arranged with faculty member. prereq: instr consent; may be repeated for cr
GDES 4371 - Data Visualization Studio
Credits: 3.0 [max 3.0]
Course Equivalencies: 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: 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 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: CSci 3081W/CSci 4018W/CSci4089
Typically offered: Every Fall & Spring
Principles of programming design/analysis. Concepts in software development. Uses a programming project to illustrate key ideas in program design/development, data structures, debugging, files, I/O, testing, and 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
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. Prerequisites: 3081w
CSCI 4511W - Introduction to Artificial Intelligence (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 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.