CS 690N, Spring 2018, UMass Amherst
Instructor: Brendan O'Connor
TA: Katie Keith
Lectures: TuTh 2:30PM - 3:45PM in Engineering Lab 303 (B2 on map)
See also: Last year's version of the course.
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. 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 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 585, 688, 689, STAT 697ML, or equivalent. Previous experience in linguistics or natural language processing may be helpful, but is not required.
Should I take both 585 and 690N? We are recommending against it for most students. First, there is some overlap in topics, so time may be wasted if you already took 585. Second, there is considerably more advanced machine learning, which 585 does not entirely prepare students for (though it helps a little bit). Third, space in the course is reserved for PhD students (since 585 already exists), so we do not know if there will be space to accommodate all interested students. All that said, there may be some cases where taking both 585 and 690N makes sense; please ask us if you have questions.
Syllabus: More details in syllabus.
See this list of courses in NLP and related areas offered at UMass and nearby.