printer friendly pageCS 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. We will introduce a common programming framework for deep learning for the problem sets.Prerequisites: programming abilities (python), linear algebra,
Math 21 or equivalent, machine learning background (
CS 229 or similar) Recommended:
CS 224N,
EE364a (convex optimization),
CS 231N
Terms: Spr
| Units: 3-4
Instructors:
Socher, R. (PI)
;
Bagadia, S. (TA)
;
Dindi, D. (TA)
;
Hong, J. (TA)
;
Ramsundar, B. (TA)
;
Yan, Q. (TA)
;
arivazhagan, N. (TA)
CS 224M: Multi-Agent Systems
For advanced undergraduates, and M.S. and beginning Ph.D. students. The course serves primarily as an introduction to game theory, including computational aspects. Topics: basic game representations and solution concepts, social choice and mechanism design, multi-agent learning, communication. Applications discussed as appropriate; emphasis is on conceptual matters and theoretical foundations. Prerequisites: very basic probability theory and optimization.
Last offered: Spring 2014
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 224S: Spoken Language Processing
Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Automatic speech recognition, extraction of affect and social meaning from speech, speech synthesis, dialogue management, and applications to digital assistants, search, and recommender systems. Prerequisites:
CS 124, 221, 224N, or 229.
Last offered: Spring 2014
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
Instructors:
MacCartney, B. (PI)
;
Potts, C. (PI)
;
Caswell, I. (TA)
...
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Instructors:
MacCartney, B. (PI)
;
Potts, C. (PI)
;
Caswell, I. (TA)
;
Huang, B. (TA)
;
Monroe, W. (TA)
;
Pai, S. (TA)
;
li, J. (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 225B: Robot Programming Laboratory
For robotics and non-robotics students. Students program mobile robots to exhibit increasingly complex behavior (simple dead reckoning and reactivity, goal-directed motion, localization, complex tasks). Topics: motor control and sensor characteristics; sensor fusion, model construction, and robust estimation; control regimes (subsumption, potential fields); probabalistic methods, including Markov localization and particle filters. Student programmed robot contest. Programming is in C++ on Unix machines, done in teams. Prerequisite: programming at the level of 106B, 106X, 205, or equivalent.
Last offered: Autumn 2011
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)
;
Alban-Hidalgo, M. (TA)
;
Caswell, I. (TA)
...
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Instructors:
Ermon, S. (PI)
;
Alban-Hidalgo, M. (TA)
;
Caswell, I. (TA)
;
How, P. (TA)
;
Hsu, L. (TA)
;
Kuleshov, V. (TA)
;
Li, X. (TA)
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