Syllabus - Probabilistic Models in Informatics

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	PDF
2	10/27	Probability Theory	PDF
3	11/10	Probability Distributions	PDF
4	11/17	Maximum Likelihood and Estimation	PDF
5	11/24	Latent Variable Models	PDF
6	12/01	Application: A Probabilistic Model of Mixed Pixels for Image Processing
7	12/08	Hidden Markov Models (1)	PDF
8	12/15	Hidden Markov Models (1)
9	12/22	Graphical Models (1)	PDF
10	01/05	Graphical Models (2)
11	01/12	Markov Chain Monte Carlo	PDF
12	01/19	Model Selection	PDF
13	01/26	Kalman Filter	PDF
	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

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

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

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 2009

Course Plan

Paper Reading

Own Experiments