PMR: 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.

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

WeekDateSlides and Required ReadingTutorial
1Tue Jan 16 
Wed Jan 17
Fri Jan 19
2Tue Jan 23
  • Directed Graphical Models - slides03b continued
 
Wed Jan 24 
Fri Jan 26 
3Tue 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:

Wed Jan 31
  • Expressive Power of Graphical Models [slides06]
Fri Feb 02
4Tue Feb 06
  • Exact inference

Tutorial 2:

Wed Feb 07
  • Exact inference
  • Exact inference for Hidden Markov Models [slides09]
Fri Feb 09
  • Exact inference for Hidden Markov Models
5Tue Feb 13

Tutorial 3:

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
6Mo Feb 26

Tutorial 4:

Comp Lab 2:

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)
7Tue Mar 05
  • Factor Analysis and Independent Component Analysis [slides12]

Tutorial 5:

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]
8Tue Mar 12
  • Variational Inference and Learning I: Fundamentals and the EM algorithm 

Tutorial 6:

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
9Tue Mar 19
  • Variational Inference and Learning II: Latent Variable Models and Variational Autoencoders [slides16]

Tutorial 7:

Comp Lab 3

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]
10Tue Mar 26
  • Sampling and Monte Carlo Integration

Tutorial 8:

Comp Lab 4:

Wed Mar 27
  • Sampling and Monte Carlo Integration
Fri Mar 29
License
Creative Commons - Attribution