Lectures related to Matrix Decomposition: Triangular and Spectral

Delves into singular value decomposition and its applications in linear algebra.

Explores matrix inversion, conditions for invertibility, uniqueness of the inverse, and elementary matrices for inversion.

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.