Course Description

Introduction to machine learning concepts, techniques, and algorithms.Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Students will gain the intuition behind modern machine learning algorithms as well as a more formal understanding of how, why, and when they work. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Instructor
Stan Sclaroff, sclaroff@cs.bu.edu Office hours: Mon/Wed 10-11, Fri 11-noon Office location: Room MCS 279
Lectures		Teaching Fellow
Tue and Thu 11-12:30 Room MCS B33		Quan Yuan, yq@cs.bu.edu Office hours: T 5-6pm and W 4-6pm Office hours location: MCS 263
Required Text
Bishop, Christopher M., Pattern Recognition and Machine Learning, Springer, 2006, ISBN 978-0-387-31073-2.
Tests		Assignments
Midterm: Thu 2/22 (in class) Final exam: Fri 5/11 9-11am		4 Programming projects 3 Problem sets