printer friendly pageCS 221: Artificial Intelligence: Principles and Techniques
Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing, Markov decision processes, graphical models, machine learning, and logic. Prerequisites:
CS 103 or
CS 103B/X,
CS 106B or
CS 106X,
CS 107, and
CS 109 (algorithms, probability, and programming experience).
Terms: Aut
| Units: 3-4
Instructors:
Liang, P. (PI)
;
FU, J. (TA)
;
Garg, A. (TA)
;
Hata, K. (TA)
;
Hsu, L. (TA)
;
Hussami, N. (TA)
;
Martinez, P. (TA)
;
NeCamp, J. (TA)
;
Pai, S. (TA)
;
Puranik, A. (TA)
;
Sadhwani, A. (TA)
;
Shen, C. (TA)
;
Wang, J. (TA)
;
Yu, N. (TA)
;
Zhou, Y. (TA)
CS 224N: Natural Language Processing (LINGUIST 284)
Methods for processing human language information and the underlying computational properties of natural languages. Syntactic and semantic processing from linguistic and algorithmic perspectives. Focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, translation, and interpretation; and representative systems. Prerequisites: CS124 or
CS121/221.
Terms: Aut
| Units: 3-4
Instructors:
Khandwala, N. (PI)
;
Manning, C. (PI)
;
Billovits, C. (TA)
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Instructors:
Khandwala, N. (PI)
;
Manning, C. (PI)
;
Billovits, C. (TA)
;
Chen, D. (TA)
;
Eric, M. (TA)
;
Huang, J. (TA)
;
Nayak, N. (TA)
;
Paranjape, A. (TA)
CS 224W: Social Information and Network Analysis
(Formerly 322) How do diseases spread? Who are the influencers? How can we predict friends and enemies in a social network? How information flows and mutates as it is passed through networks? Behind each of these questions there is an intricate wiring diagram, a network, that defines the interactions between the components. And we will never understand these questions unless we understand the networks behind them. The course will cover recent research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Class will explore how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution. Topics include methods for link analysis and network community detection, diffusion and information propagation on the web, virus outbreak detection in networks, and connections with work in the social sciences and economics.
Terms: Aut
| Units: 3-4
Instructors:
Adamic, L. (PI)
;
Althoff, C. (TA)
;
Aryan, O. (TA)
;
Bagadia, S. (TA)
;
Desai, N. (TA)
;
Lo, C. (TA)
;
Mundra, R. (TA)
;
Syminelakis, P. (TA)
CS 225A: Experimental Robotics
Hands-on laboratory course experience in robotic manipulation. Topics include robot kinematics, dynamics, control, compliance, sensor-based collision avoidance, and human-robot interfaces. Second half of class is devoted to final projects using various robotic platforms to build and demonstrate new robot task capabilities. Previous projects include the development of autonomous robot behaviors of drawing, painting, playing air hocket, yoyo, basketball, ping-pong or xylophone. Prerequisites: 223A or equivalent.
Terms: Aut, Spr
| Units: 3
CS 229: Machine Learning (STATS 229)
Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.
Terms: Aut, Spr
| Units: 3-4
Instructors:
Duchi, J. (PI)
;
Ng, A. (PI)
;
Ahluwalia, V. (TA)
;
Ahres, Y. (TA)
;
Alban-Hidalgo, M. (TA)
;
Anenberg, B. (TA)
;
Bahtchevanov, I. (TA)
;
CHU, H. (TA)
;
Corbett-Davies, S. (TA)
;
Donnat, C. (TA)
;
Du, Y. (TA)
;
Friedberg, R. (TA)
;
Guu, K. (TA)
;
Haque, A. (TA)
;
How, P. (TA)
;
Ishfaq, H. (TA)
;
Iyer, K. (TA)
;
Jiang, X. (TA)
;
Kaplow, I. (TA)
;
Lim, D. (TA)
;
Lin, Y. (TA)
;
Martinez, A. (TA)
;
McCann, B. (TA)
;
Parthasarathy, N. (TA)
;
Qin, J. (TA)
;
Rosenman, E. (TA)
;
Samar, A. (TA)
;
Sesia, M. (TA)
;
Sun, Y. (TA)
;
Tsai, S. (TA)
;
Turan, D. (TA)
;
Vyas, S. (TA)
;
Wang, H. (TA)
;
Zhou, L. (TA)
;
Zhu, M. (TA)
CS 232: Digital Image Processing (EE 368)
Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. Term project. Recommended:
EE261,
EE278.
Terms: Aut
| Units: 3
Instructors:
Wetzstein, G. (PI)
CS 238: Decision Making under Uncertainty (AA 228)
This course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications. Introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include: Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover: air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. Prerequisites: basic probability and fluency in a high-level programming language.
Terms: Aut
| Units: 3-4
CS 241: Embedded Systems Workshop
Project-centric building hardware and software for embedded computing systems. Students work on an existing project of their own or join one of these projects. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, deterministic timing and synchronization, state-based programming models, filters, frequency response, and high-frequency signals, low power operation, system and PCB design, security, and networked communication. Prerequisite:
CS107 (or equivalent).
Terms: Aut
| Units: 3
| Repeatable
3 times
(up to 9 units total)
CS 242: Programming Languages
Central concepts in modern programming languages, impact on software development, language design trade-offs, and implementation considerations. Functional, imperative, and object-oriented paradigms. Formal semantic methods and program analysis. Modern type systems, higher order functions and closures, exceptions and continuations. Modularity, object-oriented languages, and concurrency. Runtime support for language features, interoperability, and security issues. Prerequisite: 107, or experience with Lisp, C, and an object-oriented language.
Terms: Aut
| Units: 3
CS 244B: Distributed Systems
Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies. Topics: distributed shared memory, object-oriented distributed system design, distributed directory services, atomic transactions and time synchronization, application-sufficient consistency, file access, process scheduling, process migration, and storage/communication abstractions on distribution, scale, robustness in the face of failure, and security. Prerequisites:
CS 144 and
CS 249A.
Terms: Aut, Spr
| Units: 3
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