printer friendly pageCS 319: Topics in Digital 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.
| Repeatable
for credit
CS 323: Automated Reasoning: Theory and Applications
Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications.
Terms: Spr
| Units: 3-4
Instructors:
Ermon, S. (PI)
CS 327A: Advanced Robotic Manipulation
Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Prerequisites: 223A or equivalent.
Terms: Spr
| Units: 3
Instructors:
Khatib, O. (PI)
CS 328: Topics in Computer Vision
Fundamental issues of, and mathematical models for, computer vision. Sample topics: camera calibration, texture, stereo, motion, shape representation, image retrieval, experimental techniques. May be repeated for credit. Prerequisites: 205, 223B, or equivalents.
| Repeatable
for credit
CS 329: Topics in Artificial Intelligence
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.
| Repeatable
for credit
CS 329M: Topics in Artificial Intelligence: Algorithms of Advanced Machine Learning
This advanced graduate course explores in depth several important classes of algorithms in modern machine learning. We will focus on understanding the mathematical properties of these algorithms in order to gain deeper insights on when and why they perform well. We will also study applications of each algorithm on interesting, real-world settings. Topics include: spectral clustering, tensor decomposition, Hamiltonian Monte Carlo, adversarial training, and variational approximation. Students will learn mathematical techniques for analyzing these algorithms and hands-on experience in using them. We will supplement the lectures with latest papers and there will be a significant research project component to the class. Prerequisites: Probability (
CS 109), linear algebra (
Math 113), machine learning (
CS 229), and some coding experience.
Terms: Spr
| Units: 3
Instructors:
Zou, J. (PI)
CS 331B: Representation Learning in Computer Vision
This course surveys recent developments in representation learning that are relevant to visual recognition and understanding tasks. In particular we will examine: 1) why representations matter; 2) classical and moderns methods of forming and learning representations in 2D and 3D computer vision; 3) how to close the loop between sensing and action for perception robotics; 4) how to connect visual-based representations with language; 5) methods for analyzing and visualizing representations. In addition to regular lectures and talks by invited speakers, we will read advanced papers on this topic, and carry out in-depth discussions of these papers as well as the students' own research projects.
Terms: Aut
| Units: 3
CS 334A: Convex Optimization I (CME 364A, EE 364A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as
EE263, basic probability.
Terms: Spr, Sum
| Units: 3
Instructors:
Angeris, G. (PI)
;
Boyd, S. (PI)
;
Momeni, A. (PI)
;
Agrawal, A. (TA)
;
Fu, R. (TA)
;
Hemmati, S. (TA)
;
Lim, R. (TA)
;
Mohamed, A. (TA)
;
Momeni, A. (TA)
;
Sharafat, A. (TA)
;
Sheng, H. (TA)
;
Sun, Q. (TA)
;
Yu, G. (TA)
;
Zaidi, M. (TA)
;
Zhang, J. (TA)
CS 340: Topics in Computer Systems
Topics vary every quarter, and may include advanced material being taught for the first time. May be repeated for credit.
CS 341: Project in Mining Massive Data Sets
Team project in data-mining of very large-scale data, including the problem statement and implementation and evaluation of a solution. Teams consist of three students each, and they will meet regularly with a "coach" chosen from participating staff. Early lectures will cover the use of Amazon EC2 and certain systems like Hadoop and Hive. Occasional lectures thereafter will feature outside speakers, special topics of interest, and progress reports by the teams. Prerequisite:
CS 246.
Terms: Spr
| Units: 3
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