CS 21SI: AI for Social Good
Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). Taught jointly by CS+Social Good and the Stanford AI Group, the aim of the class is to empower students to apply these techniques outside of the classroom. The class will focus on techniques from machine learning and deep learning, including regression, support vector machines (SVMs), neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The course alternates between lectures on machine learning theory and discussions with invited speakers, who will challenge students to apply techniques in their social good domains. Students complete weekly coding assignments reinforcing machine learning concepts and applications. Prerequisites: programming experience at the level of
CS107, mathematical fluency at the level of
CS103, comfort with probability at the level of
CS109 (or equivalent). Application required for enrollment.
Terms: Spr
| Units: 2
Instructors:
Piech, C. (PI)
CS 109: Introduction to Probability for Computer Scientists
Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of
MATH 51 or
CME 100 or equivalent.
Terms: Aut, Spr, Sum
| Units: 3-5
| UG Reqs: WAY-AQR, WAY-FR, GER:DB-EngrAppSci
Instructors:
Piech, C. (PI)
;
Sahami, M. (PI)
;
Yan, L. (PI)
;
Banerjee, O. (TA)
;
Chartock, E. (TA)
;
Chen, J. (TA)
;
Chen, W. (TA)
;
Corcoran, B. (TA)
;
Daniel, J. (TA)
;
Dasu, G. (TA)
;
Davis, A. (TA)
;
Glaser, N. (TA)
;
Hemmati, S. (TA)
;
Istrate, A. (TA)
;
Johnston, L. (TA)
;
Kim, S. (TA)
;
Le, Y. (TA)
;
Liu, Y. (TA)
;
Moore, D. (TA)
;
Palamuttam, R. (TA)
;
Prasetio, C. (TA)
;
Redondo, E. (TA)
;
Shukla, A. (TA)
;
Ulmer, B. (TA)
;
Wright, D. (TA)
;
Yan, L. (TA)
CS 109L: Statistical Computing with R Laboratory
Supplemental lab to
CS109. Introduces the R programming language for statistical computing. Topics include basic facilities of R including mathematical, graphical, and probability functions, building simulations, introductory data fitting and machine learning. Provides exposure to the functional programming paradigm. Corequisite:
CS109.
Last offered: Spring 2015
CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Methods include: string algorithms, edit distance, language modeling, the noisy channel, naive Bayes, inverted indices, collaborative filtering, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, chatbots, sequence alignment, spell checking, speech processing, recommender systems. Prerequisites:
CS103,
CS107,
CS109.
Terms: Win
| Units: 3-4
Instructors:
Jurafsky, D. (PI)
;
Cheng, J. (TA)
;
Dozat, T. (TA)
;
Farhangi, A. (TA)
;
Garcia, G. (TA)
;
Hamilton, W. (TA)
;
Jia, R. (TA)
;
Musa, R. (TA)
;
Park, K. (TA)
;
Plattner, C. (TA)
;
Pyke, J. (TA)
;
Shen, K. (TA)
;
Tang, S. (TA)
;
Voigt, R. (TA)
;
Wang, L. (TA)
CS 250: Algebraic Error Correcting Codes (EE 387)
Introduction to the theory of error correcting codes, emphasizing algebraic constructions, and diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; Reed-Solomon and Reed-Muller codes; list-decoding, list-recovery and locality. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Prerequisites: Linear algebra, basic probability (at the level of, say,
CS109, CME106 or
EE178) and "mathematical maturity" (students will be asked to write proofs). Familiarity with finite fields will be helpful but not required.
Terms: Win
| Units: 3
Instructors:
Wootters, M. (PI)
;
Hulett, R. (TA)
EE 387: Algebraic Error Correcting Codes (CS 250)
Introduction to the theory of error correcting codes, emphasizing algebraic constructions, and diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; Reed-Solomon and Reed-Muller codes; list-decoding, list-recovery and locality. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Prerequisites: Linear algebra, basic probability (at the level of, say,
CS109, CME106 or
EE178) and "mathematical maturity" (students will be asked to write proofs). Familiarity with finite fields will be helpful but not required.
Terms: Win
| Units: 3
Instructors:
Wootters, M. (PI)
;
Hulett, R. (TA)
LINGUIST 180: From Languages to Information (CS 124, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Methods include: string algorithms, edit distance, language modeling, the noisy channel, naive Bayes, inverted indices, collaborative filtering, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, chatbots, sequence alignment, spell checking, speech processing, recommender systems. Prerequisites:
CS103,
CS107,
CS109.
Terms: Win
| Units: 3-4
Instructors:
Jurafsky, D. (PI)
;
Cheng, J. (TA)
;
Dozat, T. (TA)
...
more instructors for LINGUIST 180 »
Instructors:
Jurafsky, D. (PI)
;
Cheng, J. (TA)
;
Dozat, T. (TA)
;
Farhangi, A. (TA)
;
Garcia, G. (TA)
;
Hamilton, W. (TA)
;
Jia, R. (TA)
;
Musa, R. (TA)
;
Park, K. (TA)
;
Plattner, C. (TA)
;
Pyke, J. (TA)
;
Shen, K. (TA)
;
Tang, S. (TA)
;
Voigt, R. (TA)
;
Wang, L. (TA)
LINGUIST 280: From Languages to Information (CS 124, LINGUIST 180)
Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Methods include: string algorithms, edit distance, language modeling, the noisy channel, naive Bayes, inverted indices, collaborative filtering, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, chatbots, sequence alignment, spell checking, speech processing, recommender systems. Prerequisites:
CS103,
CS107,
CS109.
Terms: Win
| Units: 3-4
Instructors:
Jurafsky, D. (PI)
;
Cheng, J. (TA)
;
Dozat, T. (TA)
...
more instructors for LINGUIST 280 »
Instructors:
Jurafsky, D. (PI)
;
Cheng, J. (TA)
;
Dozat, T. (TA)
;
Farhangi, A. (TA)
;
Garcia, G. (TA)
;
Hamilton, W. (TA)
;
Jia, R. (TA)
;
Musa, R. (TA)
;
Park, K. (TA)
;
Plattner, C. (TA)
;
Pyke, J. (TA)
;
Shen, K. (TA)
;
Tang, S. (TA)
;
Voigt, R. (TA)
;
Wang, L. (TA)
Filter Results: