2025Year

First Semester

Special Topics in Parallel Algorithms

Lecture

Special Topics in Parallel Algorithms

Z1900024 Special Topics in Parallel Algorithms

FUJII Akihiro

2.0Credits

Wed.3Period

Shinjuku Campus

A-1161教室

A) A high degree of specialized expertise　100%
B) The skills to use science and technology　0%
C) The ability to conduct research independently, knowledge pertaining to society and occupations, and sense of ethics required of engineers and researchers　0%
D) Creative skills in specific areas of specialization　0%

This lecture provides knowledge about graduate-level linear algebra and optimization. The lecture focuses on knowledge that is related with machine learning applications.

Members of the class are assumed to have basic knowledge of linear algebra.

Other

In the lecture, students read the text book beforehand, and make a presentation on the contents in turn every week.
Subjects are as follows. Numbers show the order of lecture hours.

1-3. matrix vector product, orthorgonal vectors, norm, singular value decomposition
4-5. QR factorization, least squares method
6-8. condition number, robustness
9-10. linear equation
11-12. eigenvalue
13-14. Optimization

Reports about allocated chapters of the text book are graded.

The uncompleted parts on the class report will be feed backed.

No book is specified.

- Linear algebra and optimization for machine learning , C. C. Aggarwal, Springer.
- Numerical Linear Algebra, Lloyd N. Trefethen, David Bau. SIAM.

Office hour is from 1:30 PM to 2:30 PM on Tuesday.
Room number is A2476.

Numerical linear algebra forms a basis of many practical applications including machine learning.
Preperations and review of this lecture will help students understand the concepts of linear algebra fully.

Not applicable

Informatics Program