| Description
| 1. Introduction - Importance of Linear Algebra
2. Ax=b, Matrix, Gaussian Elimination and Application
3. Vector Spaces and Linear Equation
4. Applications: Graphs and Networks
5. Orthogonality and Least Squares
6. Applications: Multiple Regression
7. Application: Linear Programming
8. Determinant and its Applications
9. Eigenvalues and Eigenvectors
10. Ak and Difference Equations
11. eA and Differential Equations
12. Similarity Transformation and Spectral Theorem
13. Positive Definite Matrices and Minimum Principles
14. Applications: Multivariate Analysis and Principal Component Analysis
15. Singular Value Decomposition and its Applications
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