News
The HMM lecture slides are here.
Solutions to assignment 1 have been posted.
Lectures 13 and 14 (guest lecturer Amir-massoud Farahmand) have been posted.
Lectures 11 and 12 are now posted.
Two past midterms are now posted on the lectures web page.
Homework 3 is now posted. It is due October 30.
The procedure for submitting assignments has changed. You now have to upload your work using the web form provided on the assignments web page.
Notes for lecture 9 were revised and notes for lecture 10 are posted.
Notes for lecture 9 are posted
All lecture notes are now posted. Homework 2 is also posted, and homework due dates have been revised. Office hours have also been modified (see below).
For people who do not know Matlab, we suggest watching the online Matlab tutorials. A question-answer session with the TAs for Matlab help has been scheduled Thursday, September 20, in McConnell Engineering room 103.
Lecture 4 is posted on the web page. Lecture 3 slides are also posted, but will be revised, according to what was actually covered in class.
Assignment 1 is posted on the homework page. Please dump the cache of your browser to make sure that you get the 2012 assignment, not the 2011 one.
Lectures 1 and 2 are posted.
The first class takes place Thursday, September 6
General Information
Where: Trottier room 0060.When: Tuesday and Thursday, 2:35 - 3:55pm.
What: The goal of this class is to provide an overview of the state-of-art 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 data-mining 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 from elevators)
Office Hours: Tuesday and Thursday, 4:00-5:00pm. Meetings at other times by appointment only
Phone: (514) 398-6443
E-mail: dprecup@cs.mcgill.ca
Teaching assistants
Mahdi Milani Fard
Office Hours: Wednesday 12-1pm, McConnell Engineering building room 320
Mahdi Milani Fard
Office Hours: Monday 4-5pm, McConnell Engineering building room 103
TAs can be reached by e-mail at: cs652 at cs dot mcgill dot ca
References
There is no required textbook. However, the material we cover is described in several books. The schedule will include recommended reading, either from these books, or from research papers, as appropirate.- 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", McGraw-Hill, 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.