News
 The PCA slides used in class are here and here
 Homework 4 is now posted. The data is here. It is due Wednesday November 25, by midnight. There is one more homework after this. The lowest of the 5 homework grades will get dropped.
 Lecture 12 notes are posted here
 Lecture 11 notes are posted here
 The midterm, on Wed Oct. 28, will be covering material from lectures 111 (supervised learning). You are allowed one doublesided cheat sheet. Here are some example midterms from previous years: 2007, 2006 and 2005. There was no midterm in 2008.
 Homework 3 has been posted here. It is due Friday, October 23, 2009, by midnight.
 Lecture 10 slides are available here.
 Lecture 9 slides are available here
 Lecture 8 slides are available here
 Lecture 7 slides are available here
 Lecture 6 slides are available here
 Lecture 5 slides are available here
 Lecture 3 slides are available here
 Grades and comments for homework 1 have been emailed. Please notify us if you did not receive them.
 Assignment 2 is now posted: pdf, wpbcx.dat, wpbcy.dat, hw2x.dat, hw2y.dat
 Lecture 4 slides are here
 Please email assignment 1 to cs652@cs.mcgill.ca by midnight on Wed Sep 16
 Slides for lecture 2 are available here. They may be revised after class.
 Homework 1 is available here. It is due on Wednesday, September 16. The data is available in files hw1x.dat and hw1y.dat
 For a review of probability and related concepts, look at Bishop Sec. 1.2, 2.1, 2.2. and 2.3. Appendix B is a good reference for different types of distributions. For a review of linear algebra and matrix properties, look at Bishop Appendix C. Email Doina if you'd like a tutorial on either topic, or other recommended reading.
 The slides from lecture 1 are available here. If you print them, please have multiple slides per page. Reading: Bishop, Sec. 1.1. Eventualy they will move on the schedule web page, but the schedule is still under construction.
General Information
Where: McConnell Engineering building, room 103.When: Mondays and Wednesdays, 2:353:55pm.
What: The goal of this class is to provide an overview of the stateofart algorithms used in machine learning. The field of machine learning is concerned with the question of how to construct computer programs that improve automatically with experience. In recent years, many successful applications of machine learning have been developed, ranging from datamining programs that learn to detect fraudulent credit card transactions, to autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundation of this field. During this course, we will study both the theoretical properties of machine learning algorithms and their practical applications.
Instructor
Doina PrecupSchool of Computer Science
Office: McConnell Engineering building, room 111N (left fromm elevators)
Office Hours:
Phone: (514) 3986443
Email: dprecup@cs.mcgill.ca
Teaching assistants
Jordan Frank  
Office Hours:  Tuesday, 1:302:30pm, McConnell Engineering building room 108 (right from elevators) 
Email:  jordan dot frank at cs dot mcgill dot ca 
Mahdi Milani Fard  
Office Hours:  Thursday, 2:303:30pm, McConnell Engineering building room 112 (right from elevators) 
Email:  mahdi dot milanifard at mail dot mcgill dot ca 
References

The recommended references are:
 Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2006.
 Richard S. Sutton and Andrew G. Barto, "Reinforcement learning: An introduction",MIT Press, 1998.
 Tom Mitchell, "Machine Learning", McGrawHill, 1997.
 Richard O. Duda, Peter E. Hart & David G. Stork, "Pattern Classification. Second Edition", Wiley & Sons, 2001.
 Trevor Hastie, Robert Tibshirani and Jerome Friedman, "The Elements of Statistical Learning", Springer, 2009.
 David J.C. MacKay, "Information Theory, Inference and Learning Algorithms", Cambridge University Press, 2003.
 Ethem Alpaydin, "Introduction to Machine Learning", MIT Press, 2004.