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 | |
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1 | Tue Jan 16 |
Slides:
Required:
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Fri Jan 19 |
Slides:
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2 | Tue Jan 23 | Slides: Required: Optional: | |
Fri Jan 26 |
Slides:
Required:
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3 | Tue Jan 30 |
Slides:
Required:
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Tutorial 1: |
Fri Feb 02 |
Slides:
Required:
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4 | Tue Feb 06 |
Slides:
Required:
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Tutorial 2: |
Fri Feb 09 | Slides: Required: Optional: | ||
5 | Tue Feb 13 |
Slides:
Required:
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Tutorial 3: |
Fri Feb 16 |
Slides:
Required:
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6 | Tue Feb 27 |
Slides:
Required:
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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:
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8 | Tue Mar 13 |
Slides:
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Tutorial 6: |
Fri Mar 16 |
Slides:
Required:
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9 | Tue Mar 20 |
Slides:
Required:
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Tutorial 7: |
Fri Mar 23 | Slides: Optional: | ||
10 | Tue Mar 27 |
Slides:
Required:
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Tutorial 8: |
Fri Mar 30 |
Slides:
Required:
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11 | Tue Apr 03 |
Slides:
Required:
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Fri Apr 06 | Wrap-up, exam info, and Q&A |