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71 - 80 of 217 results for: CS

CS 223A: Introduction to Robotics (ME 320)

Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field. Recommended: matrix algebra.
Terms: Win | Units: 3
Instructors: Khatib, O. (PI)

CS 224D: Deep Learning for Natural Language Processing

Deep learning approaches have obtained very high performance across many different natural language processing tasks. In this class, students will learn to understand, implement, train, debug, visualize and potentially invent their own neural network models for a variety of language understanding tasks. The course provides a deep excursion from early models to cutting-edge research. Applications will range across a broad spectrum: from simple tasks like part of speech tagging, over sentiment analysis to question answering and machine translation. The final project will involve implementing a complex neural network model and applying it to a large scale NLP problem. Prerequisites: programming abilities (python), linear algebra, Math 21 or equivalent, machine learning background ( CS 229 or similar) Recommended: machine learning ( CS 229, CS 228), CS 224N, EE364a (convex optimization), CS 231N
Instructors: Socher, R. (PI)

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: Manning, C. (PI)

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

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180, CS 124, CS 224N, CS224S, or CS221; and logical/semantics such as LINGUIST 130A or B, CS 157, or PHIL150
Terms: Spr | Units: 3-4

CS 224W: Social and Information Networks

(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: 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: Aut, Spr | Units: 3
Instructors: Khatib, O. (PI)

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

CS 228: Probabilistic Graphical Models: Principles and Techniques

Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.
Terms: Win | Units: 3-4
Instructors: Ermon, S. (PI)

CS 228T: Probabilistic Graphical Models: Advanced Methods

For students interested in advanced methods in machine learning and probabilistic AI. Describes the theoretical foundations for methods of inference and learning in probabilistic graphical models, allowing for the derivation of properties of these methods and for the development of more advanced methods. Sample topics include advanced methods in Markov chain Monte Carlo, approximate message-passing algorithms for inference derived from an optimization perspective, representation and inference in models involving continuous variables, learning undirected models, learning with hidden variables, and non-parametric Bayesian methods. Prerequisites: CS228; strong mathematical foundation.
Last offered: Spring 2012

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
Instructors: Ng, A. (PI)
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