Course material

We will largely use the book Bayesian Reasoning and Machine Learning by David Barber (Cambridge University Press, 2012), together with additional material as needed. A free online version of the book is available here.

Broadly speaking, the course covers four topics:

• Probabilistic graphical models
• Exact inference
• Learning
• Approximate inference and learning

Probabilistic graphical models will be treated in more detail than in Barber’s book.

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). However, you may understand the content better if you work through the optional material too.

The course material will be added as we progress. It will be similar to last year’s material.

Programme