2020 Faculty Courses School of Science Department of Physics Graduate major in Physics
Special Topics in Physics VIII
- Academic unit or major
- Graduate major in Physics
- Instructor(s)
- Masayuki Ohzeki
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Intensive
- Class
- -
- Course Code
- PHY.P558
- Number of credits
- 100
- Course offered
- 2020
- Offered quarter
- 4Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
Recently, learning machine learning attracts attention. It is fun to try.
After acquiring a doctoral degree (physics) with statistical mechanics as the pillar, after studying it by changing its interest to the information science system, this is quite interesting.
Besides, data science including machine learning is compatible with the exact road of physics that faces experimental data.
I think that it will be good as a basic subject of undergraduate course.
You can not miss not only getting as a tool but also having fun to understand.
When a large number of degrees of freedom gather, it becomes an object of analysis with statistical mechanics as a clue,
You can bring a cut by physics into the information science methodology.
Perhaps quantum mechanics, perhaps that way, may develop further.
I'm planning a lecture that will inspire me to think about it.
Course description and aims
To understand and to become familiar with machine leanring from the perspective of statistical mechanics.
Keywords
machine learning, statistical mechanics
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Mainly in the format of lectures.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Beyond least-squares | Beyond least-squares |
| Class 2 | Approximation of functions and machine learing | Approximation of functions and machine learing |
| Class 3 | Implementation of neural net using Python and Chainer | Implementation of neural net using Python and Chainer |
| Class 4 | Deep learing and kernel method | Deep learing and kernel method |
| Class 5 | Sparse modeling | Sparse modeling |
| Class 6 | Statististical mechanics of information and spin glass theory | Statististical mechanics of information and spin glass theory |
| Class 7 | Physics and machine learing | Physics and machine learing |
Study advice (preparation and review)
Textbook(s)
none
Reference books, course materials, etc.
Distributed as appropriate.
Evaluation methods and criteria
Mainly by homework
Related courses
- PHY.S301 ： Statistical Mechanics
- PHY.S312 ： Statistical Mechanics II
- PHY.S440 ： Statistical Mechanics III
Prerequisites
Understanding of the basics of statistical mechanics.