2022 Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Mathematical and Computing Science
Topics on Mathematical and Computing Science A
- Academic unit or major
- Graduate major in Mathematical and Computing Science
- Instructor(s)
- Ayaka Sakata
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Intensive
- Class
- -
- Course Code
- MCS.T414
- Number of credits
- 200
- Course offered
- 2022
- Offered quarter
- 3-4Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In this lecture, a graphical model representing a probability distribution is treated. We will learn basics such as defining a graph and interpreting a graph as a conditional probability and practical topics such as inference of a graph and inference on a graphical model. In addition, we will treat related topics on statistics and machine learning to learn inference methods using a graphical model.
Course description and aims
The student will acquire the basics of graphical models by learning the following:
1. Basic concepts of Bayesian networks and Markov networks
2. Inference methods on graphs such as structure learning and inference of conditional probabilities
3. Effective calculations on graphs represented by belief propagation
4. Applications of a graphical model to various problems
5. Sufficient knowledge to learn advanced topics such as causal inference
Keywords
Bayesian networks, Markov networks, Structure learning, Belief propagation
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The student will learn each topic as lectures (in japanese).
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Basics of graphical model and Bayesian network 12/9(Fri) 8:50-10:30 at W833 | Understanding the contents covered by the course. |
| Class 2 | Markov network 12/9(Fri) 10:45-12:25 at W833 | Understanding the contents covered by the course. |
| Class 3 | Inference of conditional probability 12/16(Fri) 8:50-10:30 at W833 | Understanding the contents covered by the course. |
| Class 4 | Strucurture learning 12/16(Fri) 10:45-12:25 at W833 | Understanding the contents covered by the course. |
| Class 5 | Variable deletion on graphical model 12/23(Fri) 8:50-10:30 at W833 | Understanding the contents covered by the course. |
| Class 6 | Junction tree algorithm 12/23(Fri) 10:45-12:25 at W833 | Understanding the contents covered by the course. |
| Class 7 | Belief propagation on tree 1/6(Fri) 8:50-10:30 at W833 | Understanding the contents covered by the course. |
| Class 8 | Approximate inference by belief propagation 1/6(Fri) 10:45-12:25 at W833 | Understanding the contents covered by the course. |
| Class 9 | Approximate message passing for regression and sparse recovery 1/20(Fri) 8:50-10:30 at W833 | Understanding the contents covered by the course. |
| Class 10 | Density evolution 1/20(Fri) 10:45-12:25 at W833 | Understanding the contents covered by the course. |
| Class 11 | Information criterion and cross validation 1/27(Fri) 8:50-10:30 at W833 | Understanding the contents covered by the course. |
| Class 12 | Active learning 1/27(Fri) 10:45-12:25 at W833 | Understanding the contents covered by the course. |
| Class 13 | Decision theory 2/3(Fri) 8:50-10:30 at TBA | Understanding the contents covered by the course. |
| Class 14 | Reserved 2/3(Fri) 10:45-12:25 at TBA | Understanding the contents covered by the course. |
Study advice (preparation and review)
Textbook(s)
None.
Reference books, course materials, etc.
Koller & Friedman, “Probabilistic Graphical Models” MIT Press (2009)
Mézard and Montanari, “Information, Physics, and Computation,” Oxford University Press (2009).
Evaluation methods and criteria
Evaluate understanding by a report assignment.
Related courses
- MCS.T212 ： Fundamentals of Probability
- MCS.T223 ： Mathematical Statistics
Prerequisites
None.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Lecturer: Ayaka Sakata (The Institute of Statistical Mathematics) ayaka[at]ism.ac.jp
Contact professor: Satoshi Takabe (TiTech) takabe[at]c.titech.ac.jp
Other
Be sure to the lecture day.