CSC412/2506 Winter 2023

Probabilistic Machine Learning

The language of probability allows us to coherently and automatically account for uncertainty. This course will teach you how to build, fit, and do inference in probabilistic models. These models let us generate novel images and text, find meaningful latent representations of data, take advantage of large unlabeled datasets, and even let us do analogical reasoning automatically. This course will teach the basic building blocks of these models and the computational tools needed to use them.

What you will learn





No required textbooks.



Tentative Schedule

Week 1 - Jan 9th - Course Overview and Graphical Model Notation


  1. Class Intro
  2. Topics covered
  3. Exponential Family


Week 2 - Graphical Model Notation, Decision Theory and Parametrizing Probabilistic Models

Week 3 - Latent variables and Exact Inference

Week 4 - Message Passing + Sampling

Week 5 - MCMC

Week 6 - Variational Inference

Week 7 - Feb 21st & 22nd - No classes - Reading week


Week 8 - Midterm Week

Week 9 - Stochastic Variational Inference and Variational Autoencoders

Week 10 - Embeddings

Week 11 - Kernel Methods, Attention

Week 12 - Gaussian Processes

Week 13 - Diffusion, Final Exam Review