PMR: 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 exercise sheets: The exercise sheets contain both exercises to do for the tutorials and exercises for self-study and exam preparation that you can do at your own pace. The exercises for the tutorials are listed at the beginning of the sheet.

Programme

Week	Date	Slides and Required Reading	Tutorial
1	Tue Jan 16	Introduction [slides01] Reading: Introduction to Probabilistic Modelling, Sections 1 and 2
	Wed Jan 17	Basic Assumptions for Efficient Model Representation [slides02] Exercise on inference [pdf] [solution] Directed Graphical Models [slides03a]
	Fri Jan 19	Directed Graphical Models [slides03b]
2	Tue Jan 23	Directed Graphical Models - slides03b continued
	Wed Jan 24	Undirected Graphical Models [slides04a]
	Fri Jan 26	Undirected Graphical Models [slides04b]
3	Tue Jan 30	Causality and Graphical Models [slides05] [updated 01/02/24 to add slide 7, and on 07/02 to add slide 15]	Tutorial 1: Notes Questions Solutions
	Wed Jan 31	Expressive Power of Graphical Models [slides06]
	Fri Feb 02	Factor Graphs [slides07] Exact inference [slides08]
4	Tue Feb 06	Exact inference	Tutorial 2: Notes Questions Solutions
	Wed Feb 07	Exact inference Exact inference for Hidden Markov Models [slides09]
	Fri Feb 09	Exact inference for Hidden Markov Models
5	Tue Feb 13	Python notebook on HMMs (basics) Decision theory [slides10]	Tutorial 3: Notes Questions Solutions Comp Lab 1
	Wed Feb 14	No lecture
	Fri Feb 16	Guest lecture: What is the difference between PGMs and neural networks? (Dr Antonio Vergari) [slides]
No classes and tutorials in the week of Feb 19
6	Mo Feb 26	Basics of Model-Based Learning [slides11] [updated on 27 Feb to correct Eq 10, and improve explanation on slide 44] Reading: Introduction to Probabilistic Modelling, Sec 3 Note: the lecture is exceptionally on Monday (9am, AT 3) rather than on Tuesday this week	Tutorial 4: Notes Questions Solutions Comp Lab 2: HMM inference
	Wed Feb 28	Basics of Model-Based Learning Reading: Barber, Chapter 8 (w/o 8.4.2)
	Fri Mar 01	Basics of Model-Based Learning Reading: Barber, Chapter 8 (w/o 8.4.2)
7	Tue Mar 05	Factor Analysis and Independent Component Analysis [slides12]	Tutorial 5: Notes Questions Solutions
	Wed Mar 06	Factor Analysis and Independent Component Analysis Intractable Likelihood Functions [slides13]
	Fri Mar 08	Intractable Likelihood Functions Variational Inference and Learning I: Fundamentals and the EM algorithm [slides14]
8	Tue Mar 12	Variational Inference and Learning I: Fundamentals and the EM algorithm	Tutorial 6: Notes Questions Solutions
	Wed Mar 13	Variational Inference and Learning I: Fundamentals and the EM algorithm Learning for Hidden Markov Models [slides15]
	Fri Mar 15	Learning for Hidden Markov Models
9	Tue Mar 19	Variational Inference and Learning II: Latent Variable Models and Variational Autoencoders [slides16]	Tutorial 7: Notes Questions Solutions Comp Lab 3 HMM learning
	Wed Mar 20	Variational Inference and Learning II: Latent Variable Models and Variational Autoencoders
	Fri Mar 22	Variational Inference and Learning II: Latent Variable Models and Variational Autoencoders Sampling and Monte Carlo Integration [slides17]
10	Tue Mar 26	Sampling and Monte Carlo Integration	Tutorial 8: Notes Questions Solutions Comp Lab 4: VAEs
	Wed Mar 27	Sampling and Monte Carlo Integration
	Fri Mar 29	Course recap [slides18] Exam info [slides19]

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