PML: Course material | Open Course Materials

To pass the course you must understand the material provided here 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 editions of the course .

Note on the self-study exercises: These are additional exercises to support your learning and exam preparation. Work through them at your own pace. Do some to support your understanding of the lectures and tutorials. Reserve some for exam preparations (they include former exam questions). 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	Materials	Tutorial
1	Tue Sept 22	Introduction [slides] Probability Background and Basic Results [slides] [notes] [self-study exercises] [self-study solutions]
	Wed Sept 23	Probability Background and Basic Results Basic Assumptions for Efficient Model Representation [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Sept 24	Basic Assumptions for Efficient Model Representation Directed Graphical Models [slides I] [slides II] [notes] [self-study exercises] [self-study solutions]
2	Tue Sept 29	Directed Graphical Models
	Wed Sept 30	Directed Graphical Models Undirected Graphical Models [slides I] [slides II] [notes] [self-study exercises] [self-study solutions]
	Thu Oct 1	Undirected Graphical Models
3	Tue Oct 6	Undirected Graphical Models Expressive Power of Graphical Models [slides] [notes] [self-study exercises] [self-study solutions]	Tutorial 1 Questions Solutions
	Wed Oct 7	Expressive Power of Graphical Models Exact inference [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Oct 8	Exact inference
4	Tue Oct 13	Exact inference	Tutorial 2 Questions Solutions
	Wed Oct 14	Exact inference Exact inference for Hidden Markov Models [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Oct 15	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 20	Actions and their Effects (Causality) [slides] [notes] [self-study exercises] [self-study solutions]	Tutorial 3 Questions Solutions
	Wed Oct 21	Actions and their Effects (Causality) Decision making under uncertainty [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Oct 22	Decision making under uncertainty
6	Tue Oct 27	Decision making under uncertainty Basics of Model-Based Learning [slides] [notes] [self-study exercises] [self-study solutions]	Tutorial 4 Questions Solutions
	Wed Oct 28	Basics of Model-Based Learning
	Thu Oct 29	Basics of Model-Based Learning Factor Analysis and Independent Component Analysis [slides] [notes] [self-study exercises] [self-study solutions]
7	Tue Nov 3	Factor Analysis and Independent Component Analysis Intractable Likelihood Functions [slides]	Tutorial 5 Questions Solutions
	Wed Nov 4	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 Nov 5	Variational Inference and Learning I: Fundamentals, mean-field VI, and the EM algorithm
8	Tue Nov 10	Variational Inference and Learning I: Fundamentals, mean-field VI, and the EM algorithm Learning for Hidden Markov Models [slides] [notes] [self-study exercises] [self-study solutions] Optional: Python notebook on HMMs (check out the learning notebooks)	Tutorial 6 Questions Solutions
	Wed Nov 11	Learning for Hidden Markov Models
	Thu Nov 12	Learning for Hidden Markov Models Variational Inference and Learning II: Modern VI and Variational Autoencoders [slides] [notes] [self-study exercises] [self-study solutions] Optional: Python notebook on VAEs
9	Tue Nov 17	Variational Inference and Learning II: Modern VI and Variational Autoencoders	Tutorial 7 Questions Solutions
	Wed Nov 18	Variational Inference and Learning II: Modern VI and Variational Autoencoders Sampling and Monte Carlo Integration [slides] [notes] [self-study exercises] [self-study solutions]
	Thu Nov 19	Sampling and Monte Carlo Integration
10	Tue Nov 24	Sampling and Monte Carlo Integration	Tutorial 8 Questions Solutions
	Wed Nov 25	Sampling and Monte Carlo Integration
	Thu Nov 26	Course recap [slides] Exam info [slides]

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Fri, 21/08/2026 - 12:00

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