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
Week | Lecture | Tutorial | |
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1 | Mon Jan 14 |
Slides:
Required:
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Tue Jan 15 |
Slides:
Required:
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Fri Jan 18 | Slides: Required: Optional: | ||
2 | Mon Jan 21 | Slides: Required: Optional: | |
Tue Jan 22 | Slides: Required: Optional: | ||
Fri Jan 25 |
Slides:
Required:
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3 | Mon Jan 28 | Slides: Optional: |
Tutorial 1: |
Tue Jan 29 |
Slides:
Required:
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Fri Feb 01 | Slides: Required: Optional: | ||
4 | Mon Feb 04 |
Slides:
Required:
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Tutorial 2: |
Tue Feb 05 | Slides: | ||
Fri Feb 08 |
Slides:
Required:
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5 | Mon Feb 11 |
Slides:
Required:
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Tutorial 3: |
Tue Feb 12 | Slides: Required: Optional: | ||
Fri Feb 15 |
Slides:
Required:
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6 | Mon Feb 25 |
Slides:
Required:
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Tutorial 4: |
Tue Feb 26 |
Slides:
Required:
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Fri Mar 01 |
Slides:
Required:
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7 | Mon Mar 04 |
Slides:
Required:
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Tutorial 5: |
Tue Mar 05 | Slides: | ||
Fri Mar 08 |
Slides:
Required:
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8 | Mon Mar 11 |
Slides:
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Tutorial 6: |
Tue Mar 12 | Slides: | ||
Fri Mar 15 | Slides: | ||
9 | Mon Mar 18 | Slides: Optional: |
Tutorial 7: |
Tue Mar 19 | Slides: | ||
Fri Mar 22 |
Slides:
Required:
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10 | Mon Mar 25 | Slides: |
Tutorial 8: |
Tue Mar 26 |
Slides:
Required:
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Fri Mar 29 |
Slides:
Required:
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11 | Mon Apr 01 | Slides: | |
Tue Apr 02 | Slides: |