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 firstorder 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 firstorder 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 firstorder 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 firstorder logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the MyhillNerode theorem, contextfree grammars, Turing machines, decidable and recognizable languages, selfreference 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

Units: 35

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Lee, C. (PI)
;
Schwarz, K. (PI)
;
Alvarez, J. (TA)
;
Brickner, A. (TA)
;
Hoag, E. (TA)
;
Kravitz, J. (TA)
;
Le, T. (TA)
;
MayerHirshfeld, R. (TA)
;
Melloni, J. (TA)
;
Noyola, T. (TA)
;
Saini, D. (TA)
;
Saleh, M. (TA)
;
Smith, R. (TA)
;
Sriram, P. (TA)
;
Zhu, M. (TA)
CS 103A: Mathematical Problemsolving Strategies
Problem solving strategies and techniques in discrete mathematics and computer science. Additional problem solving practice for
CS103. Inclass participation required. Prerequisite: consent of instructor. Corequisite:
CS103.
Terms: Aut

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Schwarz, K. (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. Methods include: string algorithms, edit distance, language modeling, the noisy channel, machine learning classifiers, inverted indices, collaborative filtering, neural embeddings, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, spell checking, recommender systems, chatbots. Prerequisites:
CS103,
CS107,
CS109.
Terms: Win

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Jurafsky, D. (PI)
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, machine learning classifiers, inverted indices, collaborative filtering, neural embeddings, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, spell checking, recommender systems, chatbots. Prerequisites:
CS103,
CS107,
CS109.
Terms: Win

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Jurafsky, D. (PI)
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, machine learning classifiers, inverted indices, collaborative filtering, neural embeddings, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, spell checking, recommender systems, chatbots. Prerequisites:
CS103,
CS107,
CS109.
Terms: Win

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Jurafsky, D. (PI)
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