Machine Learning (COMP-652 and ECSE-608)
Winter 2015


Boyu will have office hours this week on Friday (January 23) 2-3pm, and starting in February, on Mondays (2-3pm) (MC108). Neil will have office hours Tuesdays, 1-2pm (MC111).

Slides for lecture 6 have been posted on January 21.

The data files for Assignment 1 have been posted on January 16 (see Assignments page)

Assignment 1 has been posted on January 14 (see Assignments page)

The first class takes place Tuesday, January 6.

General Information

Where: Trottier room 1080.

When: Tuesday and Thursday, 8:35-9:55am.

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.


Doina Precup
School of Computer Science
Office: McConnell Engineering building, room 111N (left from elevators)
Office Hours: Tuesday, 10-11:30am. Meetings at other times by appointment only
Phone: (514) 398-6443

Teaching assistants

Neil Girdhar (neil dot girdhar at mail dot mcgill dot ca). Office hours: Tuesday 1-2pm, MC111.

Boyu Wang (boyu dot wang at mail dot mcgill dot ca). Office hours: Monday 2-3pm, MC108.


There 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. Lecture notes and other relevant materials are linked to the lectures web page. Assignments are linked to the assignments web page

MyCourses will be used only for bulletin board, discussion groups and assignment submission and grading.