Deep Learning Course

Welcome! What you'll find here is a collection of annotated lecture notes from a deep learning course I taught.

I taught this course at the Rotman School of Management (University of Toronto) as part of the Master of Management Analytics (MMA) program from 2020 to 2025. The material hosted here is specifically from the 2025 iteration of the course.

When I first took this on, it was officially listed as a marketing analytics course. Over time, I naturally pivoted it into a deep learning course with applications in marketing. In the end, the syllabus ended up being roughly a 70/30 split between core deep learning concepts and marketing-related topics.

A few things to keep in mind if you're reading through these notes:

The Audience: The course was designed for professional master's students, typically 0 to 2 years out of their undergrad. Most of them didn't come from a pure Computer Science background, but rather technical-adjacent fields like statistics, engineering, or economics.
The Focus: My main goal was always capturing the core intuition and main ideas of each topic, rather than getting bogged down in deep math or rigorous proofs. To be honest, extracting and explaining those high-level ideas is the most fun part of teaching anyway—sometimes the math can just be tedious.
The Pace of AI: As anyone following the space knows, things have progressed incredibly fast over the last few years. Because of that, some of the material here is inevitably a bit (or a lot) out of date.

I'm putting this up mostly for posterity, but I hope it still serves as a useful resource. If you end up finding it helpful, feel free to drop me a note!

Lecture 01

Introduction to Neural Networks

20 slides

Lecture 02

Training Neural Networks