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Special course in Information Science II
Information Theory and Machine Learning, Spring 2004
Seminar program and timetable
Date |
Chapter(s) of the book |
Presenter |
Slides |
22.1. |
Initial arrangements, first meeting |
J. Karhunen |
PDF |
29.1. |
1. Introduction to Information Theory |
J.-H. Schleimer |
PDF |
5.2. |
2. Probability, Entropy, and Inference |
J. Raitio |
PDF |
12.2. |
No seminar! |
|
|
19.2. |
3. More about Inference |
J. Ahola |
PDF |
26.2. |
4. The Source Coding Theorem, and
5. Symbol Codes, and
6. Stream Codes (central results) |
T. Raiko |
PDF |
4.3. |
8. Correlated Random Variables, and
9. Communication over a Noisy Channel |
T. Hirvonen |
PDF |
11.3. |
No seminar! |
|
|
18.3. |
20. Clustering,
21. Exact Inference, and
22. Maximum Likelihood and Clustering |
A. Vyskubov |
|
18.3. |
24. Exact Marginalization,
27. Laplace's Method, and
28. Model Comparison and Occam's Razor |
A. Klami |
PDF |
25.3. |
29. Monte Carlo Methods |
T. Ukkonen |
|
1.4. |
30. Efficient Monte Carlo Methods, and
32. Exact Monte Carlo Methods |
M. Harva |
|
15.4. |
31. Ising Models, and
33. Variational methods |
J. Peltonen |
|
22.4. |
38. Neural Networks,
40. Capacity of a Single Neuron, and
41. Learning as Inference |
Z. Yang |
PDF |
29.4. |
44. Supervised Learning in Multilayer Networks, and
45. Gaussian Processes |
J. Salojärvi |
PDF |
There is no seminar on 8th April due to the Easter holiday.
The seminar is based on the book D. MacKay, Information Theory,
Inference, and Learning Algorithms, Cambridge Univ. Press, 2003.
This timetable is also available as a PDF
file.

http://www.cis.hut.fi/Opinnot/T-61.6020/2004/timetable04.shtml
antti.honkela@hut.fi
Thursday, 29-Apr-2004 16:21:20 EEST
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