PMR 2022 | Course material

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.

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 18	Introduction Reading: Background on probability and probabilistic modelling, Sections 1 and 2
	Wed Jan 19	Basic Assumptions for Efficient Model Representation Directed Graphical Models I
	Fri Jan 21	Directed Graphical Models I Directed Graphical Models II
2	Tue Jan 25	Directed Graphical Models II
	Wed Jan 26	Directed Graphical Models II Undirected Graphical Models I
	Fri Jan 28	Undirected Graphical Models I
3	Tue Feb 01	Undirected Graphical Models II	Tutorial 1 Notes Questions Solutions
	Wed Feb 02	Expressive Power of Graphical Models
	Fri Feb 04	Expressive Power of Graphical Models Factor graphs
4	Tue Feb 08	Exact inference	Tutorial 2 Notes Questions Solutions
	Wed Feb 09	Exact inference
	Fri Feb 11	Exact inference
5	Tue Feb 15	Exact inference Exact Inference for Hidden Markov Models	Tutorial 3 Notes Questions Solutions
	Wed Feb 16	Exact Inference for Hidden Markov Models Python notebook on HMMs (basics and inference)
	Fri Feb 18	Exact Inference for Hidden Markov Models
	No class or tutorials the week of Feb 21
6	Tue Mar 01	Basics of Model-Based Learning Reading: Introduction to Probabilistic Modelling, Section 3	Tutorial 4 Notes Questions Solutions
	Wed Mar 02	Basics of Model-Based Learning Reading: Barber, Chapter 8 (w/o 8.4.2)
	Fri Mar 04	Basics of Model-Based Learning Reading: Barber, Chapter 8 (w/o 8.4.2)
7	Tue Mar 08	Basics of Model-Based Learning Factor Analysis and Independent Component Analysis	Tutorial 5 Notes Questions Solutions
	Wed Mar 09	Factor Analysis and Independent Component Analysis Intractable Likelihood Functions
	Fri Mar 11	Intractable Likelihood Functions
8	Tue Mar 15	Estimating Unnormalised Models by Score Matching Sampling and Monte Carlo Integration	Tutorial 6 Notes Questions Solutions
	Wed Mar 16	Sampling and Monte Carlo Integration
	Fri Mar 18	Sampling and Monte Carlo Integration Reading: Barber, Sections 27.4 and 27.3.1
9	Tue Mar 22	Variational Inference and Learning I	Tutorial 7 Notes Questions Solutions Code
	Wed Mar 23	Variational Inference and Learning I
	Fri Mar 25	Variational Inference and Learning II
10	Tue Mar 29	Variational Inference and Learning II	Tutorial 8 Notes Questions Solutions Direct link to tutorial repo (with solutions)
	Wed Mar 30	Variational Inference and Learning II Learning for Hidden Markov Models
	Fri Apr 01	Learning for Hidden Markov Models Python notebook on HMMs (learning)
11	Tue Apr 05	Course recap Exam info