CMPT 726: Machine Learning, Fall 2019

Instructor: Greg Mori
Lectures: Monday 16:30-19:20 in B9200
TAs: Akash Abdu Jyothi <>, Lei Chen <>, Ruizhi Deng <>, Sha Hu <>, Mengyao Zhai <>
Office hours: Calendar Project office hour signup: TBD

About the course

Machine Learning is the study of computer algorithms that improve automatically through experience. It is one of the most exciting aspects of artificial intelligence, and is the basis for many of its industrial applications. It is the preferred framework for many applications, such as face detection, hand-written digit recognition, speech recognition, and credit card fraud detection.

This course is a 700-level grad course - it will be a lecture-style course and be taught from a textbook. This course will start from the basics, no prior experience in machine learning nor pattern recognition will be presumed. Students will gain hands-on experience with state of the art machine learning algorithms via programming assignments on real datasets and a course project.


The most important prerequisite for this course is a strong mathematics background. It will be possible to refresh your knowledge at the beginning of the course, but I don't want anyone to run from the room screaming if I say "eigenvector" or "covariance matrix."


Lecture Slides




Assignment submission and grading:

Links to machine learning journals and conferences: Other resources: