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1 - 4 of 4 results for: CS103

CS 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. May not be taken by students who have completed 103A,B or 103X. Prerequisite: 106A or equivalent.
Terms: Aut, Win, Spr | Units: 3-5 | UG Reqs: GER:DB-Math, WAY-FR | Grading: Letter or Credit/No Credit

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

Automated processing of less structured information: human language text and speech, web pages, social networks, genome sequences, with goal of automatically extracting meaning and structure. Methods include: string algorithms, automata and transducers, hidden Markov models, graph algorithms, XML processing. Applications such as information retrieval, text classification, social network models, machine translation, genomic sequence alignment, word meaning extraction, and speech recognition. Prerequisite: CS103, CS107, CS109.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Jurafsky, D. (PI)

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

Automated processing of less structured information: human language text and speech, web pages, social networks, genome sequences, with goal of automatically extracting meaning and structure. Methods include: string algorithms, automata and transducers, hidden Markov models, graph algorithms, XML processing. Applications such as information retrieval, text classification, social network models, machine translation, genomic sequence alignment, word meaning extraction, and speech recognition. Prerequisite: CS103, CS107, CS109.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit

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

Automated processing of less structured information: human language text and speech, web pages, social networks, genome sequences, with goal of automatically extracting meaning and structure. Methods include: string algorithms, automata and transducers, hidden Markov models, graph algorithms, XML processing. Applications such as information retrieval, text classification, social network models, machine translation, genomic sequence alignment, word meaning extraction, and speech recognition. Prerequisite: CS103, CS107, CS109.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Jurafsky, D. (PI)
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