Fundamentals of Machine Learning

Fall 2022 (COMP451)


Class time: Mondays and Wednesdays 10:05 am-11:25 am
Class location: Lea 232
Instructor: Siamak Ravanbakhsh
Office hour: Mondays and Wednesdays 11:30-12 pm
TA team:
- Benno Krojer (Benno.krojer)
- Raihan Seraj (raihan.seraj)
TA Office hours: TBD
Communication plan:
- MyCourses: course material
- Slack: discussions and all other communications (as a substitute for email)
- GitHub: code for most of the topics covered in the course

Course Description

The course will introduce the core concepts of machine learning, with an emphasis on the computational, statistical and mathematical foundations of the field. We will study models for both supervised learning and unsupervised learning, introducing these models alongside foundational machine learning concepts, such as maximum likelihood estimation, regularization, information theory, and gradient-based optimization.


This course requires programming skills (Python and NumPy), calculus (e.g., MATH 222), linear algebra (e.g., MATH 223), probability (MATH 323) and algorithms (COMP 251).

Course Material:

Assignments, slides, and other course materials are posted on myCourses.


There are no required textbook but we closely follow the presentation of the following book:
Probabilistic Machine Learning: An Introduction by Kevin Murphy (2022) ( McGill access , GitHub draft)

List of Topics Includes (subject to minor change)

Linear Classification and Regression
Maximum Likelihood
Gradient Descent Methods
Neural Networks and Deep Learning
Exemplar Based Methods
Decision Trees, Bagging and Random Forests
Dimensionality Reduction


Evaluation will be based on the following components:
- Theory and Programming Assignments (60%)
- Final Exam (40%)

If you experience barriers to learning in this course, please do not hesitate to discuss them with me. As a point of reference, you can reach the Office for Students with Disabilities at 514-398-6009.

Academic Integrity

“McGill University values academic integrity. Therefore, all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures” (see for more information). (Approved by Senate on 29 January 2003)

Language of Submission

“In accord with McGill University’s Charter of Student Rights, students in this course have the right to submit in English or in French any written work that is to be graded. This does not apply to courses in which acquiring proficiency in a language is one of the objectives.” (Approved by Senate on 21 January 2009)