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CS 546 S - Machine Learning in NLP

Campus: Urbana-Champaign


Central learning frameworks and techniques that have emerged in the field of natural language processing and found applications in several areas in text and speech processing: from information retrieval and extraction, through speech recognition to syntax, semantics and language understanding related tasks. Examination of the theoretical paradigms -- learning theoretic, probabilistic, and information theoretic -- and the relations among them, as well as the main algorithmic techniques developed within each paradigm and in key natural language applications. Course Information: Prerequisite: CS 446 and CS 473.

Special Instructions:

Option 1

Number of Required Visit(s): 0

Course Level: Graduate

Credit: 4

Term(s): Spring


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