## 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 43: Functional Programming Abstractions

This course covers the fundamentals of functional programming and algebraic type systems, and explores a selection of related programming paradigms and current research. Haskell is taught and used throughout the course, though much of the material is applicable to other languages. Material will be covered from both theoretical and practical points of view, and topics will include higher order functions, immutable data structures, algebraic data types, type inference, lenses and optics, effect systems, concurrency and parallelism, and dependent types. Prerequisites: Programming maturity and comfort with math proofs, at the levels of CS107 and
CS103.

Terms: Win
| Units: 2

Instructors:
Cain, J. (PI)

## CS 103: Mathematical Foundations of Computing

What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole prin
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What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take
CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in
CS103A. Prerequisite: CS106B or equivalent. CS106B may be taken concurrently with
CS103.

Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-FR

Instructors:
Lee, C. (PI)
;
Schwarz, K. (PI)
;
Chan, E. (TA)
;
Fang, F. (TA)
;
Fotedar, N. (TA)
;
Galczak, A. (TA)
;
Guo, J. (TA)
;
Hulett, R. (TA)
;
Li, J. (TA)
;
Melloni, J. (TA)
;
Sharp, A. (TA)
;
Smith, R. (TA)
;
Spayd, J. (TA)
;
Spyropoulos, A. (TA)
;
Yau, J. (TA)
;
de Leon, A. (TA)

## CS 103A: Mathematical Problem-solving Strategies

Problem solving strategies and techniques in discrete mathematics and computer science. Additional problem solving practice for
CS103. In-class participation required. Prerequisite: consent of instructor. Co-requisite:
CS103.

Terms: Aut, Win
| Units: 1

Instructors:
Lee, C. (PI)
;
Liu, A. (PI)

## CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (string algorithms, edit distance, language modeling, machine learning classifiers, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites:
CS103,
CS107,
CS109.

Terms: Win
| Units: 3-4
| UG Reqs: WAY-AQR

Instructors:
Jurafsky, D. (PI)
;
Cao, H. (TA)
;
Chen, J. (TA)
;
Jiang, C. (TA)
;
Kabaghe, C. (TA)
;
Karthik, A. (TA)
;
Khandelwal, U. (TA)
;
Kim, B. (TA)
;
Patel, K. (TA)
;
Reamer, S. (TA)
;
Tan, A. (TA)
;
Zhu, L. (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. Introducing methods (string algorithms, edit distance, language modeling, machine learning classifiers, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites:
CS103,
CS107,
CS109.

Terms: Win
| Units: 3-4
| UG Reqs: WAY-AQR

Instructors:
Jurafsky, D. (PI)
;
Cao, H. (TA)
;
Chen, J. (TA)
...
more instructors for LINGUIST 180 »

Instructors:
Jurafsky, D. (PI)
;
Cao, H. (TA)
;
Chen, J. (TA)
;
Jiang, C. (TA)
;
Kabaghe, C. (TA)
;
Karthik, A. (TA)
;
Khandelwal, U. (TA)
;
Kim, B. (TA)
;
Patel, K. (TA)
;
Reamer, S. (TA)
;
Tan, A. (TA)
;
Zhu, 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. Introducing methods (string algorithms, edit distance, language modeling, machine learning classifiers, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites:
CS103,
CS107,
CS109.

Terms: Win
| Units: 3-4

Instructors:
Jurafsky, D. (PI)
;
Cao, H. (TA)
;
Chen, J. (TA)
...
more instructors for LINGUIST 280 »

Instructors:
Jurafsky, D. (PI)
;
Cao, H. (TA)
;
Chen, J. (TA)
;
Jiang, C. (TA)
;
Kabaghe, C. (TA)
;
Karthik, A. (TA)
;
Khandelwal, U. (TA)
;
Kim, B. (TA)
;
Patel, K. (TA)
;
Reamer, S. (TA)
;
Tan, A. (TA)
;
Zhu, L. (TA)

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