Lectures related to Orthogonal Projections: Gram-Schmidt Method

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

Explains the step-by-step process of finding the singular value decomposition of a matrix.

Covers the calculation of eigenvalues and eigenvectors, emphasizing their significance and applications.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Explores the fundamental theorem on singular values and the formation of orthonormal bases from eigenvectors.