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Introduction to Computer Science as a field and career for computer science majors. Overview of the field and specific examples of problem areas and methods of solution.

Fundamental principles, concepts, and methods of computing, with emphasis on applications in the physical sciences and engineering. Basic problem solving and programming techniques; fundamental algorithms and data structures; use of computers in solving engineering and scientific problems. Intended for engineering and science majors. Prerequisite: One of MATH 220 or MATH 221 or MATH 231 or MATH 241.
This course satisfies the General Education Criteria for:
Quantitative Reasoning II

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Same as INFO 102. See INFO 102.

Computing as an essential tool of academic and professional activities. Functions and interrelationships of computer system components: hardware, systems and applications software, and networks. Widely used application packages such as spreadsheets and databases. Concepts and practice of programming for the solution of simple problems in different application areas. Intended for non-science and non-engineering majors. Prerequisite: MATH 112.
This course satisfies the General Education Criteria for:
Quantitative Reasoning I

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Same as IS 107 and STAT 107. See STAT 107.
This course satisfies the General Education Criteria for:
Quantitative Reasoning I

Basic concepts in computing and fundamental techniques for solving computational problems. Intended as a first course for computer science majors and others with a deep interest in computing. Credit is not given for both CS 124 and CS 125. Prerequisite: Three years of high school mathematics or MATH 112.
This course satisfies the General Education Criteria for:
Quantitative Reasoning I

Basic concepts in computing and fundamental techniques for solving computational problems. Intended as a first course for computer science majors and others with a deep interest in computing. Credit is not given for both CS 125 and CS 124. Prerequisite: Three years of high school mathematics or MATH 112.
This course satisfies the General Education Criteria for:
Quantitative Reasoning I

Fundamental principles and techniques of software development. Design, documentation, testing, and debugging software, with a significant emphasis on code review. Credit is not given for both CS 242 and CS 126. Prerequisite: CS 125. For majors only.

Continuation of CS 124. More advanced concepts in computing and techniques and approaches for solving computational problems. Prerequisite: CS 124 or CS 125.
This course satisfies the General Education Criteria for:
Quantitative Reasoning II

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Discrete mathematical structures frequently encountered in the study of Computer Science. Sets, propositions, Boolean algebra, induction, recursion, relations, functions, and graphs. Credit is not given for both CS 173 and MATH 213. Prerequisite: One of CS 124, CS 125, ECE 220; one of MATH 220, MATH 221.

Offered for honors credit in conjunction with other 100-level computer science courses taken concurrently. A special examination may be required for admission to this course. May be repeated. Prerequisite: Concurrent registration in another 100-level computer science course (see Schedule).

Topics vary. Approved for Letter and S/U grading. May be repeated.

Ethics for the computing profession. Ethical decision-making; licensing; intellectual property, freedom of information, and privacy. Credit is not given for both CS 210 and either CS 211 or ECE 316. Prerequisite: CS 225. Junior standing required.

Navigating the complex ethical and professional landscape of the computing professional: privacy, intellectual property, cybersecurity, and freedom of speech. Hands-on exercises, assignments, and discussions in which students analyze current events from perspectives in both philosophical and professional ethics. Writing professionally and technically in several writing assignments requiring peer review, workshops, and multiple rounds of editing and revising. Credit is not given for both CS 211 and CS 210 or ECE 316. Prerequisite: CS 225.
This course satisfies the General Education Criteria for:
Advanced Composition

Design and implementation of novel software solutions. Problem identification and definition; idea generation and evaluation; and software implementation, testing, and deployment. Emphasizes software development best practices—including framework selection, code review, documentation, appropriate library usage, project management, continuous integration and testing, and teamwork. Prerequisite: CS 128; credit or concurrent registration in CS 225. Restricted to majors in Computer Science undergraduate curricula only.

Data abstractions: elementary data structures (lists, stacks, queues, and trees) and their implementation using an object-oriented programming language. Solutions to a variety of computational problems such as search on graphs and trees. Elementary analysis of algorithms. Credit is not given for CS 277 if credit for CS 225 has been earned. Prerequisite: CS 126 or CS 128 or ECE 220; One of CS 173, MATH 213, MATH 347, MATH 412 or MATH 413.
This course satisfies the General Education Criteria for:
Quantitative Reasoning II

Fundamentals of computer architecture: digital logic design, working up from the logic gate level to understand the function of a simple computer; machine-level programming to understand implementation of high-level languages; performance models of modern computer architectures to enable performance optimization of software; hardware primitives for parallelism and security. Prerequisite: CS 125 or CS 128; CS 173 or MATH 213; credit or concurrent enrollment in CS 225.

Basics of computer systems. Number representations, assembly/machine language, abstract models of processors (fetch/execute, memory hierarchy), processes/process control, simple memory management, file I/O and directories, network programming, usage of cloud services. Prerequisite: CS 225.

Basics of system programming, including POSIX processes, process control, inter-process communication, synchronization, signals, simple memory management, file I/O and directories, shell programming, socket network programming, RPC programming in distributed systems, basic security mechanisms, and standard tools for systems programming such as debugging tools. Credit is not given for both CS 241 and ECE 391. Prerequisite: CS 225 and CS 233.

Intensive programming lab intended to strengthen skills in programming. Prerequisite: CS 241.

Same as IS 265 and MACS 265. See MACS 265.

Introduction to elementary concepts in algorithms and classical data structures with a focus on their applications in Data Science. Topics include algorithm analysis (ex: Big-O notation), elementary data structures (ex: lists, stacks, queues, trees, and graphs), basics of discrete algorithm design principles (ex: greedy, divide and conquer, dynamic programming), and discussion of discrete and continuous optimization. Credit is not given for CS 277 if credit for CS 225 is earned. Prerequisite: STAT 207; one of MATH 220, MATH 221, MATH 234. CS 277 cannot be taken concurrently with CS 225.

Group projects for honors credit in computer science. Sections of this course are offered in conjunction with other 200-level computer science courses taken concurrently. A special examination may be required for admission to this course. May be repeated. Prerequisite: Concurrent registration in another 200-level computer science course (see Schedule).

Introduction to the use of classical approaches in data modeling and machine learning in the context of solvings method and variants, applied to structure from motion problems; the augmented Lagrangian method and variants; interior-point methods; SMO and other specialized algorithms for support vector machines; flows and cuts as examples of primal-dual methods; dynamics programming, hidden Markov models, and parsing: 0-1 quadratic forms, max-cut, and Markov random-fields solutions. Prerequisite: CS 450 and CS 473.

Fundamentals of machine learning and signal processing as they pertain to the development of machines that can understand complex real-world signals, such as speech, images, movies, music, biological and mechanical readings, etc. Hands-on examples of how to decompose, analyze, classify, detect and consolidate signals, and examine various commonplace operations such as finding faces from camera feeds, organizing personal music collections, designing speech dialog systems and understanding movie content. 4 graduate hours. No professional credit. Prerequisite: MATH 415; CS 361 or MATH 461 or STAT 400.

Advanced topics in natural language processing, ranging from general techniques such as deep learning for NLP to specific topics such as information extraction, knowledge acquisition, dialogue systems, language grounding, and natural language generation. Review of classic as well as state-of-the-art techniques and remaining challenges, and exploration of recent proposals for meeting these challenges. Intended for graduate students doing research in natural language processing. 4 graduate hours. No professional credit. May be repeated in separate terms up to 16 hours, if topics vary. Credit towards a degree from multiple offerings of this course is not given if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 447 and CS 446 or equivalent background.

Same as IE 534. See IE 534.

Formal models and concepts in automated cognition; integrating machine learning and prior knowledge; current approaches and detailed analyses of the role of reasoning in the learning process; computational complexity and fundamental tradeoffs between expressiveness and tractability; implications for state-of-the-art artificial intelligence areas such as automated planning, the semantic web, relational learning, structured prediction, latent models, structure learning, theory formation, etc.; philosophical and psychological aspects of integrating analytic and empirical evidence. Same as ECE 548. Prerequisite: CS 440 or CS 446.

Same as PSYC 514, ANTH 514, EPSY 551, LING 570, and PHIL 514. See PSYC 514.

Numerical algorithms for parallel computers: parallel algorithms in numerical linear algebra (dense and sparse solvers for linear systems and the algebraic eigenvalue problem), numerical handling of ordinary and partial differential equations, and numerical optimization techniques. Same as CSE 512. Prerequisite: One of CS 450, CS 457, CS 555.

Numerical techniques for initial and boundary value problems in partial differential equations. Finite difference and finite element discretization techniques, direct and iterative solution methods for discrete problems, and programming techniques and usage of software packages. Same as CSE 510 and MATH 552. 4 graduate hours. No professional credit. Prerequisite: CS 450 or CS 457.

Comprehensive treatment of algebraic and multigrid iterative methods to solve systems of equations, primarily linear equations arising from discretization of partial differential equations. Same as CSE 511.

Advanced topics in numerical analysis selected from areas of current research. Same as CSE 513. May be repeated. Prerequisite: As specified for each topic offering, see Schedule or departmental course description.

Advanced topics in security and privacy problems in machine learning systems, selected from areas of current research such as: adversarial machine learning, differential privacy, game theory enabled defenses, robust learning methods, machine learning based cybercrime analysis, network intrusion detection, and malware analysis, and machine learning interpretation techniques. 4 graduate hours. No professional credit. May be repeated if topics vary. Credit is not given towards a degree from multiple offerings of this course if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 446 and CS 463 or equivalent courses, by consent of instructor. Additional prerequisites or corequisites may be specified each term. See section information.

Current research trends in computer and network security. Privacy, tamper-resistance, unwanted traffic, monitoring and surveillance, and critical infrastructure protection. Subtopics will vary depending upon current research trends. Students work in teams in close coordination with the course instructor to develop one of the topics in depth by carrying out background research and an exploratory project. Same as ECE 524. Prerequisite: CS 461 or CS 463.

In-depth coverage of advanced topics in human-computer interaction (HCI). Applied models of human performance and attention, design tools for creative design tasks, interruptions and peripheral displays, gestures, and bimanual input, and usability evaluation techniques. Students complete a research-oriented term project of their choosing. Prerequisite: CS 465.

Online social interactions occur in many arenas important to society and human well-being, but are mediated through algorithmic interventions that alter the users expectations in these social spaces. This class explores the presentation of self, the presentation of collectives, the presentation of news, and social dynamics in these online spaces--and how algorithmic intervention shapes them from the perspective of social signaling theory. Topics covered include: the evolution of algorithmic matchmaking (as in online resume/interviews and dating sites), why people share misinformation, the mitigation of trolling, ethics, and bias in social media systems. Upon completion of this course, students will have an up-to-date understanding of the design social media interfaces with incentive structures from social signaling theory. 4 graduate hours. No professional credit. Prerequisite: CS 465 or equivalent or permission of instructor. Prioritize PhD students, then others.

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Same as MATH 580. See MATH 580.

Same as MATH 581. See MATH 581.

NP-completeness, design and analysis techniques, approximation algorithms, randomized algorithms, combinatorial optimization, linear programming. Intended for graduate students in Computer Science. Same as CSE 515. 4 graduate hours. No professional credit.

Basic and advanced concepts in the design and analysis of randomized algorithms. Sampling; concentration inequalities such as Chernoff-Hoeffding bounds; probabilistic method; random walks, dimension reduction; entropy; martingales and Azuma's inequality; derandomization. Randomized algorithms for sorting and searching; graphs; geometric problems. Basics of pseudorandomness and randomized complexity classes. Prerequisite: CS 473; MATH 461 or STAT 400.

Same as MATH 584. See MATH 584.

Advanced topics in computer-aided methods for formal deduction, selected from areas of current research, such as: resolution theorem proving strategies, special relations, equational reasoning, unification theory, rewrite systems, mathematical induction, program derivation, hybrid inference systems, and programming with logic. May be repeated in separate terms. Prerequisite: As specified for each topic offering, see Schedule or departmental course description.

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Turing machines; determinism and non-determinism; time and space hierarchy theorems; speed-up and tape compression; Blum axioms; structure of complexity classes NP, P, NL, L, and PSPACE; complete problems; randomness and complexity classes RP, RL, and BPP; alternation, polynomial-time hierarchy; circuit complexity, parallel complexity, NC, and RNC; relativized computational complexity; time-space trade-offs. Same as ECE 579. Prerequisite: CS 473 or CS 475.

A theoretical CS course covering advances in algorithmic game theory. This includes study of strategic, computational, learning, dynamic, and fairness aspects of games and markets (organizations that involves rational and strategic agents). In particular, topics will include computation and complexity of equilibria, mechanism design, fair-division, dynamics in games and markets, price-of-anarchy etc.. These topics arise from applications such as online marketplaces (like Lyft, Uber, eBay, sponsored search, TaskRabbit), social networks, recommendation systems, kidney exchange, spectrum auction, etc., and thereby will prepare students for related research and/or industry jobs. 4 graduate hours. No professional credit. Prerequisite: CS 473.

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The purpose of the course is to give each student enough background and training in the area of algorithmic genomic biology so that each will be able to do research in this area, and publish papers. The main focus of the course is phylogeny (evolutionary tree) estimation, multiple sequence alignment, and genome-scale phylogenetics, which are problems that present very interesting challenges from a computational and statistical standpoint. Time permitting, we will also discuss computational problems in microbiome analysis, protein function and structure prediction, genome assembly, and even historical linguistics. Students will learn the mathematical and computational foundations in these areas, read the current literature, and do a team research project. The course is designed for doctoral students in computer science, computer engineering, bioengineering, mathematics, and statistics, and does not depend on any prior background in biology. The technical material will depend on discrete algorithms, graph theory, simulations, and probabilistic analysis of algorithms. 4 graduate hours. No professional credit. Prerequisite: CS 374 and CS 361/STAT 361, or consent of instructor.

This graduate course on bioinformatics introduces a selection of topics in computational biology and bioinformatics, with special emphasis on current problems in regulatory genomics and systems biology. Computational approaches discussed will focus on Machine Learning techniques such as Bayesian inference, graphical models, supervised learning and network analysis. Bioinformatics topics will be introduced through lectures by instructor and research paper presentations by students, and include regulatory sequence analysis, cistromics, epigenomics, regulatory network reconstruction, non-coding variant interpretation, and protein structure and function prediction. A research project involving real data analysis with techniques related to course content is mandatory and will help prepare students for bioinformatics research. 4 graduate hours. No professional credit. Prerequisite: CS 446; Credit or concurrent enrollment in CS 466; or consent of instructor.

Approximation algorithms for NP-hard problems. Basic and advanced techniques in approximation algorithm design: combinatorial algorithms; mathematical programming methods including linear and semi-definite programming, local search methods, and others. Algorithms for graphs and networks, constraint satisfaction, packing and scheduling. Prerequisite: CS 573 or consent of instructor.

Same as ECE 584. See ECE 584.

Same as ECE 519. See ECE 519.

Same as IE 519. See IE 519.

Will introduce students to the computational principles involved in autonomous vehicles, with practical labwork on an actual vehicle. Sensing topics will include vision, lidar and sonar sensing, including state-of-the-art methods for detection, classification, and segmentation. Bayesian filtering methods will be covered in the context of both SLAM and visual tracking. Planning and control topics will cover vehicle dynamics models, state-lattice planning, sampling-based kinodynamic planning, optimal control and trajectory optimization, and some reinforcement learning. Evaluation will involve ambitious challenge projects implemented on a physical vehicle. 4 graduate hours. No professional credit. Prerequisite: CS 374, ECE 484, or equivalent.

Seminar on topics of current interest as announced in the Class Schedule. Approved for S/U grading only. May be repeated in the same or separate terms if topics vary. Prerequisite: As specified for each topic offering, see Class Schedule or departmental course description.

Individual study or reading in a subject not covered in normal course offerings. May be repeated. Prerequisite: Consent of instructor.

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. May be repeated in the same or separate terms if topics vary.

Approved for S/U grading only. May be repeated.