printer friendly pageCS 103: Mathematical Foundations of Computing
Mathematical foundations required for computer science, including propositional predicate logic, induction, sets, functions, and relations. Formal language theory, including regular expressions, grammars, finite automata, Turing machines, and NP-completeness. Mathematical rigor, proof techniques, and applications. Prerequisite: 106A or equivalent.
Terms: Aut, Win, Spr
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-FR
Instructors:
Dill, D. (PI)
;
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, genome sequences, social networks, or any less structured information. Methods include: string algorithms, edit distance, language modeling, naive Bayes, inverted indices, vector semantics. Applications such as question answering, sentiment analysis, information retrevial, text classification, social network models, machine translation, genomic sequence alignment, spell checking, speech processing. Prerequisite:
CS103,
CS107,
CS109.
Terms: Win
| Units: 3-4
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, genome sequences, social networks, or any less structured information. Methods include: string algorithms, edit distance, language modeling, naive Bayes, inverted indices, vector semantics. Applications such as question answering, sentiment analysis, information retrevial, text classification, social network models, machine translation, genomic sequence alignment, spell checking, speech processing. Prerequisite:
CS103,
CS107,
CS109.
Terms: Win
| Units: 3-4
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, genome sequences, social networks, or any less structured information. Methods include: string algorithms, edit distance, language modeling, naive Bayes, inverted indices, vector semantics. Applications such as question answering, sentiment analysis, information retrevial, text classification, social network models, machine translation, genomic sequence alignment, spell checking, speech processing. Prerequisite:
CS103,
CS107,
CS109.
Terms: Win
| Units: 3-4
Instructors:
Jurafsky, D. (PI)
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