Course details
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1- Linear Combinations of vectors and independence
2- Understanding different types ofsolution for Ax=b , for different shapes of A
3- Gaussian Elimination And Gauss Inverse
4- Matrices as linear transformations (Rotation - scale .. etc) with a large number of examples
5- Eigen Values and Eigen Vectors
6- Matrix Diagonalization and Exponentiation
7- Solution of Systems of first Order differential equations
8- The Four Spaces Of A Matrix (Column Space - Row Space - Null Space - Left Null Space)
9 - Variance - Covariance - Covariance Matrix
10 - Lagrange Multipliers
11 - PCA(The Principal Component Analysis)
12 - Singular Value Decomposition
13 - LU - LDU - LUPdecomposition
14 - Positive Definite Matrices
Updated on 14 November, 201813 - LU - LDU - LUPdecomposition
14 - Positive Definite Matrices
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