Syllabus - Probabilistic Models in Informatics

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	PDF
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

Pick up one paper, describe how the authors use probabilistic models, and the advantage of using probabilistic models.
15-min presentation, and 15-min discussion.

Own Experiments

Pick up one problem, describe how you use probabilistic models, and what kind of results you obtained.
15-min presentation, and 15-min discussion.

Syllabus - Probabilistic Models in Informatics - Second Semester 2011

Course Plan

Paper Reading

Own Experiments