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Course Catalog

This catalog is a list of courses that the department may offer in a given year. Not all courses in the catalog are offered every year.

If you are looking for current course information:
-View the Registrar's course offerings page for courses offered this semester.
-Students and faculty can refer to the Undergraduate Announcement for the academic year undergraduate course information.

Undergraduate courses are in the 100 - 400 listing. Graduate courses are in the 400 - 500 listing.

COS462 - Design of Very Large Scale Integrated (VLSI) Systems (Fall)

COS463 - Wireless Networks (Spring)

This course surveys the design and implementation of wireless networks, from underlying digital signals, through bits and error control methods, to their interaction with overlying inter-networking protocols. The course will provide an introduction to the wireless physical layer in a way that is accessible for students with solely a computer systems and networking background. Students will gain an understanding of the principles and techniques behind the design of modern wireless local- and wide-area networks, as well as their interaction with the rest of the Internet.

Cross-listed as ELE463 Department of Electrical Engineering.

Classes: Two 90-minute lectures and one 50 minute precept
Prerequisite(s): Prerequisite - COS 217. Knowledge of C and one other programming language helpful, but not required.

COS475 - Computer Architecture (Fall)

COS485 - Neural Networks: Theory and Application (Spring)

Organization of synaptic connectivity as the basis of neural computation and learning. Multilayer perceptrons, convolutional networks, and recurrent networks. Backpropagation and Hebbian learning. Models of perception, language, memory, and neural development.

Classes: Two 90-minute lectures and one 50 minute precept
Prerequisite(s): Familiarity with linear algebra, multivariate calculus, and probability theory. Knowledge of Python recommended.

COS487 - Theory of Computation (Fall)

Introduction to computability and complexity theory. Topics will include models of computation such as finite automata, pushdown automata, and Turing machines; decidability and decidability; computational complexity; P, NP, and NP completeness; others.

Cross-listed as MAT407 Department of Mathematics.

Classes: Two lectures, one precept.
Prerequisite(s): COS 340 or equivalent mathematical maturity..

COS488 - Introduction to Analytic Combinatorics (Spring)

Analytic Combinatorics aims to enable precise quantitative predictions of the properties of large combinatorial structures. The theory has emerged over recent decades as essential both for the scientific analysis of algorithms in computer science and for the study of scientific models in many other disciplines. This course combines motivation for the study of the field with an introduction to underlying techniques, by covering as applications the analysis of numerous fundamental algorithms from computer science. The second half of the course introduces Analytic Combinatorics, starting from basic principles.

Cross-listed as MAT474.

Prerequisite(s): COS 226, 340 or equivalent background in mathematics.

COS495 - Special Topics in Computer Science (Spring)

These courses cover one or more advanced topics in computer science. The courses are offered only when there is an opportunity to present material not included in the established curriculum; the subjects vary from term to term.

Cross-listed as EGR485.

Classes: Three classes.

COS496 - Special Topics in Computer Science (Fall)

These courses cover one or more advanced topics in computer science. The courses are offered only when there is an opportunity to present material not included in the established curriculum; the subjects vary from term to term.

Complex networks arise through the analysis of complex systems in many areas of study. Well known areas include social network analysis (e.g. Facebook friends), text citation analysis (e.g. Wikipedia) and biological network analysis (e.g. protein-protein interactions). Complex networks can be distinguished from random networks and from regular networks, such as grids, which are often created by design for applications such as interconnecting computers. This course examines methods of analysis of complex networks and how this analysis can be applied to enhance our understanding of real-world systems.

Cross-listed as .

Prerequisite(s): COS 226 and some experience with linear algebra..

COS497 - Senior Independent Work (B.S.E. candidates only) (Fall)

Offered in the fall, seniors are provided with an opportunity to concentrate on a "state-of-the-art" project in computer science. Topics may be selected from suggestions by faculty members or proposed by the student.

Prerequisite(s): B.S.E. candidates only.

COS498 - Senior Independent Work (B.S.E. candidates only) (Spring)

Offered in the spring, seniors are provided with an opportunity to concentrate on a "state-of-the-art" project in computer science. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty advisor.

Prerequisite(s): B.S.E. candidates only.

COS510 - Programming Languages

Logic and formal reasoning about software, treating programs and programming languages as mathematical objects about which precise claims can be made. Basic concepts and techniques such as operational semantics and axiomatic semantics for specifying programming languages; structure, definition and properties of type systems; invariants and assertions for specifying programs. Use of automated tools such as interactive proof assistants, model checkers, and/or satisfiability-modulo-theories solvers. (Replaces COS 441 beginning AY2012-2013).

Classes: Undergraduates: COS326 or permission of instructor. Graduates: none.
Prerequisite(s): COS226, COS320

COS511 - Theoretical Machine Learning

This course introduces theoretical machine learning, including mathematical models of machine learning, and the design and rigorous analysis of learning algorithms. Likely topics include: intro to statistical learning theory and bounds on the number of random examples needed to learn; learning in adversarial settings and the on-line learning model; using convex optimization to model and solve learning problems; learning with partial observability; recommendation systems and matrix learning; how to boost the accuracy of a weak learning algorithm; universal portfolio selection; learning in games.

COS516 - Automated Reasoning about Software (Fall)

An introduction to algorithmic techniques for reasoning about software. Basic concepts in logic-based techniques including model checking, invariant generation, symbolic execution, and syntax-guided synthesis; automatic decision procedures in modern solvers for Boolean Satisfiability (SAT) and Satisfiability Modulo Theory (SMT); and their applications in automated verification, analysis, and synthesis of software. Emphasis on algorithms and automatic tools.

Cross-listed as 516 Department of Electrical Engineering.

Classes: Two 90-minute lectures
Prerequisite(s): COS 226 and COS 326 (or equivalent programming experience).

COS518 - Advanced Computer Systems

Survey of operating systems covering: early systems, virtual memory, protection, synchronization, process management, scheduling, input/output, file systems, virtual machines, performance analysis, software engineering, user interfaces, distributed systems, networks, current operating systems, case studies. Survey of research papers from classic literature through contemporary research.

Prerequisite(s): COS 318 or equivalent

COS521 - Advanced Algorithm Design

Gives a broad exposure to algorithmic design ideas of the past few decades, and brings students up to a level where they can understand research papers in algorithms. Although designed for computer science grads, it may be suitable for advanced undergrads and non-CS grads as well.

The course is thematically distinct from undergrad algorithms (such as COS 423) in its extensive use of ideas such as randomness, optimization and approximation, and high dimensional geometry, which are increasingly important in applications. It also introduces other concerns that arise today, such as dealing with uncertainty, big data sizes, and strategic (i.e., game-theoretic) behaviors. All necessary mathematical tools will be covered in class.

Classes: Two lectures/week.
Prerequisite(s): One undergraduate course in algorithms (eg COS 423), or equivalent mathematical maturity. Listeners and auditors are welcome with prior permission.

COS522 - Computational Complexity

Introduction to research in computational complexity theory. Computational models: nondeterministic, alternating, and probabilistic machines. Boolean circuits. Complexity classes associated with these models: NP, Polynomial hierarchy, BPP, P/poly, etc. Complete problems. Interactive proof systems and probabilistically checkable proofs: IP=PSPACE and NP=PCP (log n, 1). Definitions of randomness. Pseudorandomness and derandomizations. Lower bounds for concrete models such as algebraic decision trees, bounded-depth circuits, and monotone circuits.

COS525 - Mathematical Analysis of Algorithms

Methods for determining the average-case performance of fundamental algorithms; ordinary and exponential generating functions, real asymptotics, complex asymptotics, singularity analysis, and Mellin transforms; and application to the analysis of Quicksort, hashing, binary tree search, digital search, communication protocols, multidimensional search, set merging, and other combinatorial algorithms. The course is intended to survey the major approaches and applications and to serve as an introduction to research in the field.

COS526 - Advanced Computer Graphics

Advanced topics in computer graphics, with a focus on learning recent methods in rendering, modeling, and animation. Appropriate for students who have taken COS 426 or equivalent and would like further exposure to computer graphics.

COS526 - Advanced Computer Graphics (Fall)

Advanced topics in computer graphics, with focus on learning recent methods in rendering, modeling, and animation. Appropriate for students who have taken COS426 (or equivalent) and who would like further exposure to computer graphics.

Classes: Two 90-minute lectures

COS527 - Probabilistic Algorithms

Construction and analysis of algorithms that solve various problems efficiently in a probabilistic sense; algorithms that work almost always and for almost all inputs; expected performance of heuristic algorithms; and fundamental limitations on probabilistic computations and other complexity issues.

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