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61 - 70 of 204 results for: CS

CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)

Machine understanding of human language. Computational semantics (determination of word sense and synonymy, event structure and thematic roles, time, aspect, causation, compositional semantics, scopal operators), and computational pragmatics and discourse (coherence, coreference resolution, information packaging, dialogue structure). Theoretical issues, online resources, and relevance to applications including question answering and summarization. Prerequisites: one of LINGUIST 180 / CS 124 / CS 224N,S: and logic such as LINGUIST 130A or B, CS 157, or PHIL150).
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 224W: Social and Information Network Analysis

(Formerly 322) How do rumors and information spread? Who are the influencers? Can we predict friendships on Facebook? Networks are the core of the WWW, blogs, Twitter and Facebook. They can be characterized by the complex interplay between information content, millions of individuals and organizations that create it, and the technology that supports it. Course will focus on how to analyze the structure and dynamics of large networks, how to model links, and how design algorithms that work with such large networks. Topics: statistical properties of large networks, models of social network structure and evolution, link prediction, network community detection, diffusion of innovation, information propagation, six-degrees of separation, finding influential nodes in networks, disease outbreak detection, networks with positive and negative ties, and connections with work in the social sciences and economics.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Leskovec, J. (PI)

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: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 227B: General Game Playing

A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

CS 229: Machine Learning

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 | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Ng, A. (PI)

CS 229T: Statistical Learning Theory (STATS 231)

(Same as STATS 231) How do we formalize what it means for an algorithm to learn from data? This course focuses on developing mathematical tools for answering this question. We will present various common learning algorithms and prove theoretical guarantees about them. Topics include online learning, kernel methods, generalization bounds (uniform convergence), and spectral methods. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning ( STATS 315A or CS 229). Convex optimization ( EE 364a) is helpful but not required.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Liang, P. (PI)

CS 240H: Functional Systems in Haskell

Covers an array of practical issues and techniques that arise when building real-world systems in the Haskell programming language. Topics include the basics of Haskell, laziness, monads, parsers, testing and debugging, performance tuning, interfacing to native code, concurrency and I/O paradigms, language extensions, meta-programming, and applications to the web and security. Concepts will be reinforced through a few individual programming assignments followed by a larger team project. Prior familiarity with Haskell may be helpful but is not required. Prerequisites: CS106B or 106X.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Mazieres, D. (PI)

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 | Grading: Letter or Credit/No Credit

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: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Cheriton, D. (PI)

CS 245: Database Systems Principles

File organization and access, buffer management, performance analysis, and storage management. Database system architecture, query optimization, transaction management, recovery, concurrency control. Reliability, protection, and integrity. Design and management issues. Prerequisites: 145, 161.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
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