The Graduate University for Advanced Studies (SOKENDAI), Department of Informatics
Second Semester 2005
Probabilistic Models in Informatics
17:00-18:30, 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 10/19 Introduction to Probability
2 10/26 Probability Theory and Bayes Theorem
3 11/02 Random Variable and Probability Distribution
4 11/09 Multivariate Probability Distribution
5 11/16 Maximum Likelihood
6 11/30 Mixture Models
7 12/07 Model Selection
8 12/14 Hidden Markov Model (1)
9 12/21 Hidden Markov Model (2)
10 01/11 Graphical Models (1)
11 01/18 Graphical Models (2)
12 01/25 Bayesian Network
13 02/01 Sampling and Simulation
02/08 ***Cancel***
14 02/15 Summary

Report 1

Paper Reading Assignment

1 01/11 Duy Le An adaptive color-based particle filter, Image Vision Computing, 2003
2 01/18 Masoomeh Torabzadeh On the Self-Similar Nature of Ethernet Traffic, IEEE/ACM Trans. Networking, 1994
3 01/25 Sano Masanori An Integrated Baseball Digest System Using Maximum Entropy Method, ACM Multimedia 2002
4 02/01 Nguyen Phuoc Tat Dat Tagging English Text with a Probabilistic Model, 1994
5 02/15 Nguyen Luu Thuy Ngan Probabilistic Reasoning for Entity & Relation Recognition, COLING'02