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 | ||

Wed Jan 17 | |||

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: |

Wed Jan 31 | - Expressive Power of Graphical Models [slides06]
| ||

Fri Feb 02 | |||

4 | Tue 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
| ||

5 | Tue Feb 13 | - Python notebook on HMMs (basics)
- Decision theory [slides10]
| Tutorial 3: |

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: 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)
| ||

7 | Tue 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]
| ||

8 | Tue 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
| ||

9 | Tue 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]
| ||

10 | Tue Mar 26 | - Sampling and Monte Carlo Integration
| Tutorial 8: Comp Lab 4: |

Wed Mar 27 | - Sampling and Monte Carlo Integration
| ||

Fri Mar 29 |