Homework 1 deadline has been extended to Monday, February 6, 10am. All other homework deadlines remain the same.
The data for homework 1 has been posted on January 25.
Homework 1 is now posted, due February 2.
Starting on Tuesday January 24, class will be held in MacDonald 279 instead ot TR 0070, which will provide enough space for all those registered. Class on Thursday January 19 is still in TR 0070.
The assignment and project schedules have been modified on Thursday, January 19. Specifically, the first assignment will become available on January 19. Assignemnts are still due every other week (not taking into account the spring break).
The first class takes place Thursday, Jaunary 5, 2017.
General InformationWhere: Trottier, room 0070
When: Tuesday and Thursday, 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, and computer vision programs that can recognize thousands of different object types. 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: Tuesday and Thursday, 2:30-3:00pm Meetings at other times by appointment only
Phone: (514) 398-6443
School of Computer Science
Office: McConnell Engineering building, room 104N (left from elevators)
Office Hours: Mondays 11:00-12:00am. Meetings at other times by appointment only
Office: McConnell Engineering building, room 107 (right from elevators)
Office Hours: Wednesdays 2:00-3:00pm. Meetings at other times by appointment only
Additional TA TBA
ReferencesThere is no required textbook. However, there are several good machine learning textbooks describing parts of the material that we will cover. The schedule will include recommended reading, either from these books, or from research papers, as appropriate.
- Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2006.
- Richard S. Sutton and Andrew G. Barto, "Reinforcement learning: An introduction", MIT Press, 1998.
- 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.
- Kevin P. Murphy, "Machine Leanring: a Probabilistic Perspective", MIT Press, 2012.
MyCourses will be used only for bulletin board, discussion groups and assignment submission and grading.