Machine Learning (COMP-652 and ECSE-608)
Winter 2016


Homework 2 has been posted on February 13 and is due February 26.

Homework 1 deadline has been extended to Friday January 29, 11:55pm.

Doina's regular office hours will be cancelled on Thursday January 28. She will instead be available in the afternoon 3:30-4:00pm.

Assignment 1 is now available, due January 28. TA information has been updated below.

Lecture 1 slides have been revised to reflect that we only discussed part of the previous deck in class. Lecture 2 slides have been posted.

Class has been moved to ENGMD 280 (McDonald Engineering Building), starting on January 14, 2016, due to lack of space in the previous room

The first class takes place Tuesday, January 12, 2016

General Information

Where: MacDonald Engineering, room 280

When: Tuesday and Thursday, 10:05-11:25am.

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 and Thursday, 11:30-12:00. Meetings at other times by appointment only
Phone: (514) 398-6443

Teaching assistants

Ryan Lowe ( Office hours Wednesdays 10:30-11:30, McConnell Engineering room 112

Boyu Wang ( Office hours Thursdyas 2:00-3:00pm, McConnell Engineering room 108.


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.