The Graduate University for Advanced Studies (SOKENDAI), Department of Informatics
Second Semester 2013
Probabilistic Models in Informatics
16:30-18:00, Tuesday
The focus of this course is probabilistic models, which play an important role for the modeling of real-world data in various fields of informatics. This course deals with foundations and applications of probability theory, and discusses basic ideas of probabilistic models, such as latent variable models and Bayesian Networks. This year we will read "Pattern Recognition and Machine Learning" by C.M. Bishop.

Course Plan

1 10/15 Introduction Material
2 10/22 Probability Distribution Material
3 10/29 Linear Models for Regression Material
4 11/05 Linear Models for Classification Material
5 11/12 Neural Networks Material
6 11/19 Kernel Methods Material
7 11/26 Sparse Kernel Machines Material
8 12/03 Paper Reading Sessions
9 12/10 Cancel
10 01/07 Graphical Models (1) Material
11 01/14 Graphical Models (2) Material
12 01/21 Graphical Models (3) Material
13 01/28 Mixture Models and EM Material
14 02/04 Real Problem Material
15 02/18 Research Presentation Sessions

Paper Reading

Research Presentation