|Lecturer||D.Sc. Antti Honkela|
|Assistant||M.Sc. Sami Hanhijärvi|
|Semester||Autumn 2006 (during periods I and II)|
|Seminar sessions|| On Mondays at 14-16 in lecture hall T4 in computer science building,|
Konemiehentie 2, Otaniemi, Espoo. The first session is on September 11, 2006.
|t616010 (at) cis.hut.fi|
Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. Much of the recent work on kernel methods for support vector machines (SVMs) is directly applicable to Gaussian processes.
This course is based on a recent textbook Gaussian Processes
for Machine Learning by
Basic knowledge on pattern recognition (e.g. T-61.3020 Principles of Pattern Recognition) and Bayesian inference methods (e.g. T-61.5040 Learning Models and Methods) will be helpful.
Each student gives a presentation in the seminar. In addition, requirements include solving sufficiently many exercise problems and making a project work, as well as active participation in the lectures (one absence is allowed).
The course mostly follows the book Gaussian Processes for Machine Learning by Carl E. Rasmussen and Christopher K. I. Williams (MIT Press, 2006). In addition to the book, some other material will be discussed.
For more information, please send email to the course organisers
(t616010 (at) cis.hut.fi).
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