「線性代數」學習對象以大一學生為主。本課程進行中, 會盡可能闡述定義與理論背後的涵義. 讓學生了解抽象敘述與實際應用的關聯。
旨在從理論與應用兩方面介紹線性代數，讓學生了解線性代數在工程與科學領域的應用。
國立中正大學資工系 柯仁松副教授
研究專長
- 無線感測網路
- 普及計算
- 行動計算
- 分散式系統
1.1-Linear Systems
1.2-Gauss method
1.3-Iterative equation-solving methods
2.1-Matrices
2.2-Matrix Algebraic Properties
2.3-Determinants
2.4-Evaluating Determinants
2.5-combinatorial definition of determinants
2.6-Elementary Matrices and Invertible Matrices
2.7-Properties of determinants
2.8-LU-Decomposition
3.1-Vector space
3.2-Subspace
3.3-Linear independence
3.4-Basis and dimension
3.5-Row space and column space
3.6-Null Space
4.1-Inner product
4.2-Norm and angle
4.3-Orthogonality
4.4-Orthonormal basis
4.5-Gram-Schmidt process
4.6-QR-decomposition
4.7-Best approximation and least squares
4.8-Least Square Data Fitting
4.9-Application to correlation
5.1-Eigenvectors and eigenvalue
5.2-Diagonalization
5.3-Application To Discrete Dynamical Systems
5.4-Application to differential equations
5.5-Iterative estimates for eigenvalues
6.1-Orthogonal matrices
6.2-Orthogonal Diagonalization
6.3-Quadratic forms
1.2-Gauss method
1.3-Iterative equation-solving methods
2.1-Matrices
2.2-Matrix Algebraic Properties
2.3-Determinants
2.4-Evaluating Determinants
2.5-combinatorial definition of determinants
2.6-Elementary Matrices and Invertible Matrices
2.7-Properties of determinants
2.8-LU-Decomposition
3.1-Vector space
3.2-Subspace
3.3-Linear independence
3.4-Basis and dimension
3.5-Row space and column space
3.6-Null Space
4.1-Inner product
4.2-Norm and angle
4.3-Orthogonality
4.4-Orthonormal basis
4.5-Gram-Schmidt process
4.6-QR-decomposition
4.7-Best approximation and least squares
4.8-Least Square Data Fitting
4.9-Application to correlation
5.1-Eigenvectors and eigenvalue
5.2-Diagonalization
5.3-Application To Discrete Dynamical Systems
5.4-Application to differential equations
5.5-Iterative estimates for eigenvalues
6.1-Orthogonal matrices
6.2-Orthogonal Diagonalization
6.3-Quadratic forms
線上課程
