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
|
Grading: Letter or Credit/No Credit
Instructors:
Liang, P. (PI)
;
Caswell, I. (TA)
;
FU, J. (TA)
;
Garg, A. (TA)
...
more instructors for CS 221 »
Instructors:
Liang, P. (PI)
;
Caswell, I. (TA)
;
FU, J. (TA)
;
Garg, A. (TA)
;
Hata, K. (TA)
;
Hsu, I. (TA)
;
Hsu, L. (TA)
;
Hussami, N. (TA)
;
Jia, R. (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 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
|
Grading: Letter or Credit/No Credit
CS 231B: The Cutting Edge of Computer Vision
(Formerly 223C) More than one-third of the brain is engaged in visual processing, the most sophisticated human sensory system. Yet visual recognition technology has fundamentally influenced our lives on the same scale and scope as text-based technology has, thanks to Google, Twitter, Facebook, etc. This course is designed for those students who are interested in cutting edge computer vision research, and/or are aspiring to be an entrepreneur using vision technology. Course will guide students through the design and implementation of three core vision technologies: segmentation, detection and classification on three highly practical, real-world problems. Course will focus on teaching the fundamental theory, detailed algorithms, practical engineering insights, and guide them to develop state-of-the-art systems evaluated based on the most modern and standard benchmark datasets. Prerequisites: CS2223B or equivalent and a good machine learning background (i.e.
CS221,
CS228,
CS229). Fluency in Matlab and C/C++.
Terms: Spr
|
Units: 3
|
Grading: Letter or Credit/No Credit
Instructors:
Li, F. (PI)
LINGUIST 188: Natural Language Understanding (CS 224U, 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
|
Grading: Letter or Credit/No Credit
LINGUIST 288: Natural Language Understanding (CS 224U, LINGUIST 188)
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
|
Grading: Letter or Credit/No Credit
Filter Results: