CS 685, Spring 2020, UMass Amherst
Instructor: Brendan O'Connor
Description: This course covers a broad range of advanced level topics in natural language processing. It is intended for graduate students in computer science who have familiarity with machine learning fundamentals, and previous course or research experience in natural language processing. It may also be appropriate for computationally sophisticated students in linguistics and related areas. Topics include probabilistic models of language, computationally tractable linguistic representations for syntax and semantics, neural network models for language, and selected topics in discourse and text mining. After completing the course, students should be able to read and evaluate current NLP research papers. Coursework includes a research literature review, homework assignments, and a final project.
Prerequisites: Familiarity with multivariate calculus, probability theory, dynamic programming, and implementation of machine learning algorithms, such as from COMPSCI 688, 689, or equivalent. Experience in linguistics will be helpful, but not required.
See also previous versions of this course: 2018 version (a.k.a. 690N), and 2019 Deep Learning for NLP.
Should I take both 685 and (585 or 690D)? No, because there is too much overlap.
See this list of courses in NLP and related areas offered at UMass and nearby.