CS 344E: Advanced Wireless Networks
Networking research in wireless systems. Topics include: multichannel/multiradio systems, routing, coding, physical layer hints, low power, mesh networking, interference cancellation, technological trends, and protocol design. Students implement and test research ideas on SWAN, a WiFi testbed.
Terms: not given this year

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
CS 348B: Computer Graphics: Image Synthesis Techniques
Intermediate level, emphasizing highquality image synthesis algorithms and systems issues in rendering. Topics include: Reyes and advanced rasterization, including motion blur and depth of field; ray tracing and physically based rendering; Monte Carlo algorithms for rendering, including direct illumination and global illumination; path tracing and photon mapping; surface reflection and light source models; volume rendering and subsurface scattering; SIMD and multicore parallelism for rendering. Written assignments and programming projects. Prerequisite: 248 or equivalent. Recommended: Fourier analysis or digital signal processing.
Terms: Spr

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Pharr, M. (PI)
CS 349: Topics in Programming Systems
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
CS 349C: Topics in Programming Systems: Readings in Distributed Systems
Discussion of research publications that are of current interest in distributed systems. Students are expected to read all papers, and sign up for presentation of one paper. The course itself is 1 unit. Those interested in working on a project along with the readings should enroll for 3 units.
Terms: not given this year

Units: 13

Grading: Letter or Credit/No Credit
CS 359: Topics in the Theory of Computation
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
CS 361A: Advanced Algorithms
Advanced data structures: unionfind, selfadjusting data structures and amortized analysis, dynamic trees, Fibonacci heaps, universal hash function and sparse hash tables, persistent data structures. Advanced combinatorial algorithms: algebraic (matrix and polynomial) algorithms, number theoretic algorithms, group theoretic algorithms and graph isomorphism, online algorithms and competitive analysis, strings and pattern matching, heuristic and probabilistic analysis (TSP, satisfiability, cliques, colorings), local search algorithms. May be repeated for credit. Prerequisite: 161 or 261, or equivalent.
Terms: not given this year

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
CS 361B: Advanced Algorithms
Topics: fundamental techniques used in the development of exact and approximate algorithms for combinational optimization problems such as generalized flow, multicommodity flow, sparsest cuts, generalized Steiner trees, load balancing, and scheduling. Using linear programming, emphasis is on LP duality for design and analysis of approximation algorithms; interior point methods for LP. Techniques for development of strongly polynomial algorithms. Prerequisites: 161 or 261, or equivalent.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Plotkin, S. (PI)
CS 362: Algorithmic Frontiers: Effective Algorithms for Large Data
The increasing sizes of the data sets around us have sparked an effort from the theory community to develop new frameworks for understanding algorithms. In many cases, the questions have become "What can we compute in time sublinear in the size of data?" or "What can we compute if we are given only several passes over the data, with relatively little memory?". In a related vein, we are also facing the challenge of trying to understand extremely large, and complex objectsfrom social networks and gene interactions, to complex distributions over enormous domainsin these settings, we often only have access to a tiny fraction of the underlying object we hope to understand. What properties of graphs and distributions can be inferred from such sparse samples? The course will cover a variety of topics at this research frontier; topics will include the theoretical work on property testing, sampling, sketching, and streaming. Prerequisites: A good background in probability theory, linear algebra, and algorithms. A high level of mathematical maturity will be assumed.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Valiant, G. (PI)
CS 364B: Topics in Algorithmic Game Theory
Topics on the interface of computer science and game theory. May be repeated for credit. Prerequisites: 364A or instructor permission.
Terms: Win

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors:
Roughgarden, T. (PI)
CS 369: Topics in Analysis of Algorithms
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
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