CS 546 - Machine Learning in NLP

Official Description

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.

Subject Area

Robotics, Vision, and Artificial Intelligence