Lectures related to Singular Value Decomposition

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

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

Explores diagonalization of symmetric matrices and their eigenvalues, emphasizing orthogonal properties.

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