MATH 462 Machine Learning

3 credits · Mathematics and Statistics · Faculty of Science · Not scheduled for 2026–2027

Introduction to supervised learning: decision trees, nearest neighbors, linear models, neural networks. Probabilistic learning: logistic regression, Bayesian methods, naive Bayes. Classification with linear models and convex losses. Unsupervised learning: PCA, k-means, encoders, and decoders. Statis…

Prerequisites

(MATH 236 or MATH 247 or MATH 251) and (MATH 248 or MATH 314 or MATH 358) and (COMP 202 or equivalent or permission of the instructor).

Prerequisite courses: COMP 202, MATH 236, MATH 247, MATH 248, MATH 251, MATH 314, MATH 358

Restrictions

Not open to students who have taken or are taking COMP 451 or COMP 551.

Courses that need MATH 462

Plan your whole degree in myDegree. Fit MATH 462 and everything it needs into a plan, term by term. Open myDegree

Source: McGill Course Catalogue. myDegree is an independent student project, not affiliated with McGill University.