▶Japanese
Post to SNS
- Home
- > School of Science
- > Graduate major in Physics
- > Advanced Special Topics in Physics VIII
- School of Science
-
School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Physics
- Instructor(s)
- Ohzeki Masayuki
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- -
- Course number
- PHY.P658
- Credits
- 1
- Academic year
- 2018
- Offered quarter
- 4Q
- Syllabus updated
- 2018/11/30
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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.
Student learning outcomes
To understand and to become familiar with machine leanring from the perspective of statistical mechanics.
Keywords
machine learning, statistical mechanics
Competencies that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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 |
Textbook(s)
one
Reference books, course materials, etc.
Distributed as appropriate.
Assessment criteria and methods
Mainly by homework
Related courses
- PHY.S301 ： Statistical Mechanics
- PHY.S312 ： Statistical Mechanics II
- PHY.S440 ： Statistical Mechanics III
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Understanding of the basics of statistical mechanics.
