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(2019)

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Linear Algebra

 NAKAMIYA. Masaki
  Requisites  2 credits
【Aerospace Engineering･1st semester】
19-1-0231-5099

１．
Outline
This course covers matrix theory and linear algebra, which are a branch of mathematics and are useful in natural science and engineering.
２．
Objectives
After successfully completing the course, you will have a good understanding of the following topics and their applications:
- Matrix operations, including inverses
- Linear dependence and independence
- Determinants and their properties
- Cramer's Rule
- Eigenvalues and eigenvectors
- Diagonalization of a matrix
- Linear transformations
３．
Grading Policy
Homework: 20%, Mid-term exam: 40%, Final exam: 40%
４．
Textbook and Reference
Textbook 　：　田代嘉宏 『工科の数学 線形代数』（森北出版）ISBN-13: 978-4627049222
Reference　：　薩摩 順吉、四ツ谷 晶二　『キーポイント線形代数』（岩波書店）ISBN-13: 978-4000078627
５．
Requirements (Assignments)
６．
Note
７．
Schedule
１. Overview, Vector
２. Definition of a matrix
３. Matrix operations
４. Linear transformations
５. Composition of linear transformations
６. Inverse matrix
７. Linear dependence and independence
８. Mid-term exam
９. Determinants
10. Cofactor expansion
11. Cramer's Rule
12. Eigenvalue, Rank
13. Diagonalization of a matrix
14. Inner product space
15. Symmetric and Orthogonal matrices
１．
Outline
This course covers matrix theory and linear algebra, which are a branch of mathematics and are useful in natural science and engineering.
２．
Objectives
After successfully completing the course, you will have a good understanding of the following topics and their applications:
- Matrix operations, including inverses
- Linear dependence and independence
- Determinants and their properties
- Cramer's Rule
- Eigenvalues and eigenvectors
- Diagonalization of a matrix
- Linear transformations
３．
Grading Policy
Homework: 20%, Mid-term exam: 40%, Final exam: 40%
４．
Textbook and Reference
Textbook 　：　田代嘉宏 『工科の数学 線形代数』（森北出版）ISBN-13: 978-4627049222
Reference　：　薩摩 順吉、四ツ谷 晶二　『キーポイント線形代数』（岩波書店）ISBN-13: 978-4000078627
５．
Requirements (Assignments)
６．
Note
７．
Schedule
１. Overview, Vector
２. Definition of a matrix
３. Matrix operations
４. Linear transformations
５. Composition of linear transformations
６. Inverse matrix
７. Linear dependence and independence
８. Mid-term exam
９. Determinants
10. Cofactor expansion
11. Cramer's Rule
12. Eigenvalue, Rank
13. Diagonalization of a matrix
14. Inner product space
15. Symmetric and Orthogonal matrices