Assignment 1 is now available on the assignments web page.
Matlab will be required for some of the assignments. For people who do not know Matlab, we suggest watching the online Matlab tutorials.
The first class takes place Monday, January 6.
General InformationWhere: Trottier room 1090.
When: Monday and Wednesday, 1:05-2:25pm..
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.
School of Computer Science
Office: McConnell Engineering building, room 111N (left from elevators)
Office Hours: Monday and Wednesday, 2:30-3:00pm, as well as Monday 10:30am-12:00pm. Meetings at other times by appointment only
Phone: (514) 398-6443
Teaching assistantsNeil Girdhar (neil dot girdhar at mail dot mcgill dot ca). Office hours: Thursday, 3-4pm, McConnell Engineering room 111 (NOT 111N!)
Boyu Wang (boyu dot wang at mail dot mcgill dot ca). Office hours: Tuesday 2-3pm, McConnell Engieering room 108.
ReferencesThere 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.
MyCourses will be used only for bulletin board, discussion groups and assignment submission and grading.