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/Tue 3-4, Thu 5-6
Office location: Room MCS 140E
 



Lectures

Teaching Fellow
Tue and Thu 11-12:30
Room PSY B35
Vitaly Ablavsky, ablavsky@cs.bu.edu
Office hours: Mon/Wed 4-5:30
Office hours location: MCS B46


 

Required Text
Bishop, Christopher M., Pattern Recognition and Machine Learning, Springer, 2006.


Optional Texts on Reserve at Science and Engineering Library
  1. Duda, Richard O., Hart, Peter E., and Stork, David G., Pattern Classification, Wiley, 2001.
  2. Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome, The Elements of Statistical Learning, Springer-Verlag, 2001.
  3. MacKay, David J. C., Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003.

Tests


Assignments
Midterm: Tur 2/24 (in class)
Final exam: Thu 4/30 (24-hour take-home)
4 Programming projects
3 Problem sets