PMR 2025: Course Material | Open Course Materials

To pass the course you must understand the material on the slides, the tutorials, and the material listed below as “required” (unless marked as not examinable). Working through some of the optional material may help you to better understand the content.

The material will be added as we proceed. For reference, please see the previous edition of PMR.

Note on the self-study exercises: These are additional exercises to support your learning and exam preparation. Work through them at your own pace. Try to solve each exercise yourself first. If you get stuck, that's fine, consult the solution gradually, starting with the next step. Try to understand why you got stuck; was it a concept or a technical step? To help you generalise, after each exercise, (1) connect it to relevant lecture concepts and (2) note any useful techniques/tricks in a "technical log" to review before the exam.

Schedule

Week	Date	Slides and Required Material	Tutorial
1	Tue Sep 16	Introduction: [slides] Basic Assumptions for Efficient Model Representation: [slides] Reading: Introduction to Probabilistic Modelling, Sections 1 and 2
	Wed Sep 17	Basic Assumptions for Efficient Model Representation Directed Graphical Models [slides I] [slides II] [notes] [self-study exercises] [self-study solutions]
	Thu Sep 18	Directed Graphical Models
2	Tue Sep 23	Directed Graphical Models
	Wed Sep 24	Directed Graphical Models Undirected Graphical Models [slides I] [slides II] [notes] [self-study exercises] [self-study solutions] (Sept 25: Slides I were updated to clarify the definition of a pairwise Markov network and to fix the explanation of why a Gaussian is one. Oct 7: Slides I were updated to insert the missing Lambda in the inner product of the mu's in Eqs (9-12). )
	Thu Sep 25	Undirected Graphical Models
3	Tue Sep 30	Undirected Graphical Models Expressive Power of Graphical Models [slides] [notes] [self-study exercises] [self-study solutions] (Oct 2: Updated slides to clarify p30 on I-equivalence of undirected graphs and DAGs, and added a new example on p31.)	Tutorial 1 Questions Solutions
	Wed Oct 01	Expressive Power of Graphical Models Exact inference [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Oct 02	Exact inference
4	Tue Oct 07	Exact inference	Tutorial 2 Questions Solutions
	Wed Oct 08	Exact inference Exact inference for Hidden Markov Models [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Oct 09	Exact inference for Hidden Markov Models Optional: Python notebook on HMMs (check out the basics and inference notebooks for a simple language model and how to run inference on it)
5	Tue Oct 14	Actions and their Effects (Causality) [slides] [notes] [self-study exercises] [self-study solutions]	Tutorial 3 Questions Solutions
	Wed Oct 15	Actions and their Effects (Causality) Decision making under uncertainty [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Oct 16	Decision making under uncertainty
6	Tue Oct 21	Decision making under uncertainty Basics of Model-Based Learning [slides] [notes] [self-study exercises] [self-study solutions]	Tutorial 4 Questions Solutions
	Wed Oct 22	Basics of Model-Based Learning
	Thu Oct 23	Basics of Model-Based Learning Factor Analysis and Independent Component Analysis [slides] [notes] [self-study exercises] [self-study solutions]
7	Tue Oct 28	Factor Analysis and Independent Component Analysis Intractable Likelihood Functions [slides]	Tutorial 5 Questions Solutions
	Wed Oct 29	Intractable Likelihood Functions Variational Inference and Learning I: Fundamentals, mean-field VI, and the EM algorithm [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Oct 30	Variational Inference and Learning I: Fundamentals, mean-field VI, and the EM algorithm
8	Tue Nov 04	Variational Inference and Learning I: Fundamentals, mean-field VI, and the EM algorithm	Tutorial 6 Questions Solutions
	Wed Nov 05	Variational Inference and Learning I: Fundamentals, mean-field VI, and the EM algorithm Learning for Hidden Markov Models
	Thu Nov 06	Learning for Hidden Markov Models [slides] [notes] [self-study exercises] [self-study solutions] Optional: Python notebook on HMMs (check out the learning notebooks)
9	Tue Nov 11	Variational Inference and Learning II: Latent Variable Models and Variational Autoencoders	Tutorial 7 Questions Solutions
	Wed Nov 12	Variational Inference and Learning II: Latent Variable Models and Variational Autoencoders
	Thu Nov 13	Variational Inference and Learning II: Latent Variable Models and Variational Autoencoders Sampling and Monte Carlo Integration
10	Tue Nov 18	Sampling and Monte Carlo Integration	Tutorial 8 Questions Solutions
	Wed Nov 19	Sampling and Monte Carlo Integration
	Thu Nov 20	Course recap Exam info
11	Tue Nov 25	Buffer
	Wed Nov 26	Buffer
	Thu Nov 27	Buffer

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