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.
Programme
Week  Lecture  Tutorial  

1  Tue Jan 16 
Slides:
Required:


Fri Jan 19 
Slides:


2  Tue Jan 23  Slides: Required: Optional:  
Fri Jan 26 
Slides:
Required:


3  Tue Jan 30 
Slides:
Required:

Tutorial 1: 
Fri Feb 02 
Slides:
Required:


4  Tue Feb 06 
Slides:
Required:

Tutorial 2: 
Fri Feb 09  Slides: Required: Optional:  
5  Tue Feb 13 
Slides:
Required:

Tutorial 3: 
Fri Feb 16 
Slides:
Required:




6  Tue Feb 27 
Slides:
Required:

Tutorial 4: 
Fri Mar 02  University closed because of adverse weather conditions; lecture cancelled  
7  Tue Mar 06  Slides: 
Tutorial 5: 
Fri Mar 09 
Slides:
Required:


8  Tue Mar 13 
Slides:

Tutorial 6: 
Fri Mar 16 
Slides:
Required:


9  Tue Mar 20 
Slides:
Required:

Tutorial 7: 
Fri Mar 23  Slides: Optional:  
10  Tue Mar 27 
Slides:
Required:

Tutorial 8: 
Fri Mar 30 
Slides:
Required:


11  Tue Apr 03 
Slides:
Required:


Fri Apr 06  Wrapup, exam info, and Q&A 