MATH 462, Fall 2022

Honours Mathematics for Machine Learning,

Professor: Adam Oberman

Class: Monday, Wednesday, 10:05 am-11:25 am BURN 1214

Office Hours Fall 2022: BURN 1106, MW 11:30-12:00, and by appointment. Additional office hours TBA.


Math and Stats Majors/Honours students, Computer Science students.

Course Description:

A mathematically rigorous approach to Machine Learning (ML). This course will cover the mathematical models which go into current machine learning models, as well as deep learning architectures, and application areas. It will provide the necessary background for understanding deep learning models and reading contemporary research papers. The sequel, Math 562, will go more deeply into mathematical aspects, such as statistical learning theory, and regularization.

Course Webpages


08/31 (Weds): Discuss course, AI vs ML, Clustering intro

09/07 (Weds): k-means clustering, losses, hypothesis classes

09/12 (Monday): Vector Calculus Review, Vector Calc for ML: sigmoids. 09/14 (Wednesday): Vector Calc for ML. Generative and discriminative models.

Homework 1, posted Sept 20, due Sept 27 (in myCourses) (updated Sept 27)

09/14 (Monday) and 09/21 (Weds)

09/26 (Monday)

09/28 (Wednesday): Programming languages for the class, using Python and Collab. K-means code.

Homework 2, revised Oct 7

10/03 (Monday)

10/05 (Wednesday)

10/13 (Thursday) first lecture after reading break

10/17 (Monday)

10/19 (Wednesday)

Homework 3, final version posted.

10/24 (Monday), 10/26 (no lecture), 10/31 (Monday)

11/2 (Weds)

11/7 (Monday): Features (handwritten notes)

11/9 (Weds): midterm

11/14 (Monday)

11/16 (Weds)

11/21 (Monday)

11/23 (Wednesday)

HW 4 Posted

11/28 (Monday)

HW 5 HW5.pdf