CSC 412/2506 Fall 2026:
Probabilistic Machine Learning
This course introduces probabilistic learning tools such as exponential families, directed graphical models, Markov random fields, exact inference techniques, message passing, sampling and mcmc, hidden Markov models, variational inference, EM algorithm, Bayesian regression, probabilistic PCA, Neural networks kernel methods, Gaussian processes, variational autoencoders, and diffusion models. It will also offer a broad view of model-building and optimization techniques that are based on probabilistic building blocks which will serve as a foundation for more advanced machine learning courses.
More details can be found in syllabus and piazza.
Announcements:
- Assignment 1 is out, and due on 10/05 23:59. TA office hours are 09/30 18-19, 10/02 18-19 at Pratt bldg. room 286.
- Please note that instructor office hours take place in the Pratt BUILDING (room 290G), not the Pratt library.
Instructors:
| Prof | Ohad Shamir |
|---|---|
| csc412prof@cs.toronto.edu | |
| Office hours | Tu 13-15 @Pratt 290G |
Teaching Assistants:
Lance (Chen-Hao) Chao, Yongjin Yang, Yuchong Zhang
- Email: csc412ta@cs.toronto.edu
Time & Location:
| Section | Lecture | Tutorial |
|---|---|---|
| CSC412 LEC0101 & CSC2506 LEC0101 | Tu 9-11 @ MP 134 | Th 10-11 @ ES B149 |
Suggested Reading
No required textbooks. Suggested reading will be posted after each lecture (See lectures below).
- (PRML) Christopher M. Bishop (2006) Pattern Recognition and Machine Learning
- (MLPP) Kevin P. Murphy (2012), Machine Learning: A Probabilistic Perspective
- (PML1) Kevin P. Murphy (2022), Probabilistic Machine Learning: An Introduction
- (PML2) Kevin P. Murphy (2023), Probabilistic Machine Learning: Advanced topics
- (ITIL) David MacKay (2003) Information Theory, Inference, and Learning Algorithms
- (ESL) Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009) The Elements of Statistical Learning
Lectures and timeline
| Week | Lectures | Suggested reading | Tutorials | Timeline |
|---|---|---|---|---|
| 1 | Introduction Probabilistic Models |
MLPP 1 & 2 PRML 2.4 |
notes slides |
syllabus |
| 2 | Decision theory Directed Graphical Models |
PRML 1.5 MLPP 10 |
notes slides |
|
| 3 | Markov Random Fields Exact inference |
MLPP 19-19.5, 20.3 ITIL 21.1, 26 |
notes | A1 out |
Assignments
| Assignment # | Out | Due | TA Office Hours |
|---|---|---|---|
| Assignment 1 | 9/22 | 10/05 | 9/30 18-19, 10/02 18-19 at Pratt bldg. 286 |
| Assignment 2 | 10/06 | 10/19 | TBA |
| Assignment 3 | 11/03 | 11/23 | TBA |
Computing Resources
For the assignments, we will use Python, and libraries such as NumPy, SciPy, and scikit-learn. You have two options:
- The easiest option is run everything on colab.
- Alternatively, you can install everything yourself on your own machine.
- If you don’t already have python, install using Anaconda.
- Use pip to install the required packages
pip install scipy numpy autograd matplotlib jupyter sklearn
- For those unfamiliar with Numpy, there are many good resources, e.g. Numpy tutorial and Numpy Quickstart.