The Graduate University for Advanced Studies (SOKENDAI), Department of Informatics
Second Semester 2009
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 10/20 Introduction
2 10/27 Probability Theory
3 11/10 Probability Distributions
4 11/17 Maximum Likelihood and Estimation
5 11/24 Latent Variable Models
6 12/01 Application: A Probabilistic Model of Mixed Pixels for Image Processing
7 12/08 Hidden Markov Models (1)
8 12/15 Hidden Markov Models (1)
9 12/22 Graphical Models (1)
10 01/05 Graphical Models (2)
11 01/12 Markov Chain Monte Carlo
12 01/19 Model Selection
13 01/26 Kalman Filter
02/02 Canceled for the entrance exam
14 02/09 Sequential Monte Carlo
15 02/16 Occasional date

Paper Reading

1 12/08 OKUNO, Keisuke Association of Whole Body Motion from Tool Knowledge for Humanoid Robots
2 12/15 NGO, Thanh Duc Visual Categorization with Bag of Keypoints
3 12/22 MUNASINGHE Lankeshwara Statistical Models for Networks: A Brief Review of Some Recent Research
4 01/05 Jain, Raghvendra

Own Experiments

1 01/12 OKUNO, Keisuke
2 01/19 NGO, Thanh Duc
3 01/26 MUNASINGHE Lankeshwara
4 02/09 Jain, Raghvendra