MATH 462: Mathematics for Machine Learning

A mathematically rigorous approach to machine learning.

Audience

Math and Stats Majors/Honours students, CS students.

Prerequisites

Textbook/References

Grading

E.g. if you missed one assignment and one midterm, with an average of 87% on the rest, then the penalty would be 87-(1+2) = 84%.

Key Dates

Refer to McGill key Dates

Lecture Notes : MATH 462, Fall 2021

Course Notes (typeset)

Homework

Lectures Part 4 Reinforcement Learning

Project

Lectures Part 3

Weeks 8 and 9, Oct 27 and 29th, Nov 3rd and 5th

Week 7, Lectures

Week 5, Lecture 9 and 10, Week 6, Lecture 11 and 12

Lectures Part 2

Week 3, Lecture 6, Sept 17 (F)

Week 4, Lecture 7, Sept 22 (W) and Week 4, Lecture 8, Sept 24 (F)

Lectures Part 1

Week 1, lecture 1, Sept 1 (W)

Week 1, Lecture 2, Sept 3 (F)

Week 2, Lecture 3, Sept 8 (W)

Week 2, Lecture 4, Sept 10 (F)

Week 3, Lecture 5, Sept 15 (W)

Earlier handwritten in class notes

Zoom recordings