The Graduate University for Advanced Studies (SOKENDAI), Department of Informatics
Second Semester 2011
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.

Course Plan

1 10/11 Introduction
2 10/18 Probability Theory
3 10/25 Probability Distributions
4 11/1 Maximum Likelihood and Estimation
5 11/8 Latent Variable Models
11/15 Cancel
6 11/22 Application: A Probabilistic Model of Mixed Pixels for Image Processing
7 11/29 Student Report (Paper Reading)
8 12/6 Hidden Markov Models (1)
9 12/13 Hidden Markov Models (2)
10 12/20 Graphical Models (1)
11 01/10 Student Report (Paper Reading)
12 01/17 Graphical Models (2)
13 01/24 Model Selection
14 01/31 Kalman Filter and Sequential Monte Carlo
15 02/07 Markov Chain Monte Carlo
16 02/14 Student Report (Presentation)

Paper Reading

Own Experiments