2020 Special Topics in Physics VIII

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Academic unit or major
Graduate major in Physics
Instructor(s)
Ohzeki Masayuki 
Course component(s)
Lecture
Mode of instruction
 
Day/Period(Room No.)
-
Group
-
Course number
PHY.P558
Credits
1
Academic year
2020
Offered quarter
3Q
Syllabus updated
2020/3/24
Lecture notes updated
-
Language used
English
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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)

none

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.

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