News
The second homework is now posted, and it is due Tuesday October 11 by midnight.
Mahdi will do a tutorial on Wednesday, 6pm, MC 103, on linear algebra and calculus. Note that the tutorials are not mandatory.
The first homework is posted, and it is due Tuesday, September 20 by midnight.
The first class takes place Thursday, September 1.
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
Philip Bachman
Office Hours: Wednesday 4:00-5:00pm, McConnell Engineering building, room 111 (right from the elevators)
E-mail:
Yuri Grinberg
Office Hours: Friday 2:30-3:30pm, McConnell Engineering building room 112 (right from elevators)
E-mail:
Mahdi Milani Fard
Office Hours: Monday, 4:00-5:00pm, McConnell Engineering building room 112 (right from elevators)
E-mail:
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", 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.