- Lecturer
- Yamashita Yukihiko
- Place
- Thr7-8(S611)
- Credits
- Lecture2 Exercise0 Experiment0
- Code
- 70042
- Syllabus updated
- 2012/9/21
- Lecture notes updated
- 2012/9/21
- Semester
- Fall Semester
Outline of lecture
Linear algebra (eigenvalue problem, singular value decomposition,
and generalized inverse matrix), optimization technique (gradient method, conjugate gradient method, and quasi-Newton method), and statistics (estimation and test) are lectured.
Purpose of lecture
The objective of this course is to provide fundamental optimization technique
and statistics to handle various quantities with respect to international development.
In order to understand those knowledges, basic mathematics for them is also provided.
Plan of lecture
1. Introduction
2. Eigenvalue decomposition and singular value decomposition
3. Generalized inverses of matrix
4. Maximum gradient method and Conjugate gradient method
5. Newton method and Quasi-Newton method
6. Lagrange窶冱 method and Karush–Kuhn–Tucker condition
7. Dual problem
8. Penalty method
9. Probability (Definition of Random variable)
10. Estimator (Maximum likelihood estimator and Bayesian estimator)
11. Cramer-Rao lower bound
12 Principle component analysis
13. Regression
14. Testing
15. Statistical learning theory
Textbook and reference
Copy of slide is prepared
Related and/or prerequisite courses
Linear algebra
Statistics
Evaluation
Attendance
Homework
Report
