10-683/11-747 Machine Learning for Text Mining
Instructors: Tom Mitchell, Jon Baxter, William Cohen, Andrew McCallum,
Fernando Pereira
Fall 2000

Prerequistes: a previous course in Machine Learning
Time: TR 10:30-11:50, course begins on Sept 14.
Place: TBD
Units: 12

Extracting useful knowledge from large amounts of text and hypertext has
become a topic of great interest, in part because of the huge volume of
information that is now available on the web. This course will overview a
variety of problems and the latest methods for text mining. We will
consider machine learning approaches to problems such as document
classification, information extraction, wrapper induction, reference
matching and combining existing symbolic databases with other text
databases. We will cover a variety of learning methods including nearest
neighbor, Bayesian methods, hidden Markov models, active learning, and
semi-supervised learning. The course format will include team-taught
lectures, reading and discussing research papers, and projects.