PMR 2023 | 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 17	Introduction Reading: Background on probability and probabilistic modelling, Sections 1 and 2
	Wed Jan 18	Basic Assumptions for Efficient Model Representation Directed Graphical Models I
	Fri Jan 20	Directed Graphical Models I Directed Graphical Models II
2	Tue Jan 24	Directed Graphical Models II
	Wed Jan 25	Directed Graphical Models II Undirected Graphical Models I
	Fri Jan 27	Undirected Graphical Models I Undirected Graphical Models II
3	Tue Jan 31	Undirected Graphical Models II Factor graphs Exact inference	Tutorial 1 Notes Questions Solutions
	Wed Feb 01	No lecture (UCU action)
	Fri Feb 03	Exact inference
4	Tue Feb 07	Exact inference	Tutorial 2 Notes Questions Solutions
	Wed Feb 08	Exact inference Exact Inference for Hidden Markov Models
	Fri Feb 10	No lecture (UCU action)
5	Tue Feb 14	No lecture (UCU action)	Tutorial 3 Notes Questions Solutions
	Wed Feb 15	No lecture (UCU action)
	Fri Feb 17	Exact Inference for Hidden Markov Models Python notebook on HMMs (basics)
	No class or tutorials the week of Feb 20
6	Tue Feb 28	Basics of Model-Based Learning Reading: Introduction to Probabilistic Modelling, Section 3	Tutorial 4 Notes Questions Solutions
	Wed Mar 01	Basics of Model-Based Learning Reading: Barber, Chapter 8 (w/o 8.4.2)
	Fri Mar 03	Basics of Model-Based Learning Reading: Barber, Chapter 8 (w/o 8.4.2)
7	Tue Mar 07	Factor Analysis and Independent Component Analysis	Tutorial 5 Notes Questions Solutions
	Wed Mar 08	Factor Analysis and Independent Component Analysis Variational Inference and Learning I
	Fri Mar 10	Variational Inference and Learning I
8	Tue Mar 14	Variational Inference and Learning I	Tutorial 6 Notes Questions Solutions Comp lab HMM inference
	Wed Mar 15	No lecture (UCU action)
	Fri Mar 17	No lecture (UCU action)
9	Tue Mar 21	No lecture (UCU action)	Tutorial 7 Notes Questions Solutions
	Wed Mar 22	No lecture (UCU action)
	Fri Mar 24	Learning for Hidden Markov Models
10	Tue Mar 28	Learning for Hidden Markov Models Variational Inference and Learning II	Tutorial 8 Notes Questions Solutions Comp lab HMM learning
	Wed Mar 29	Variational Inference and Learning II
	Fri Mar 31	Variational Inference and Learning II Python notebook on VAEs
11	Tue Apr 04	Course recap Exam info