Linear Algebra is about working on linear systems of equations. Rather than working with scalars, we start working with matrices and vectors.
Linear Algebra is the key to understanding the calculus and statistics you need in machine learning. If you can understand machine learning methods at the level of vectors and matrices, you will improve your intuition for how and when they work. The better linear algebra will lift your game across the board.
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Implementation of matrix and its methods such as finding the determinant, normalization
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Numerical optimization methods:
- Direct methods:
- Gauss Method
- Crammer's Rule
- Iterative methods:
- Jacobi method
- Gauss Seidel method