Lectures related to Decomposition Spectral: Symmetric Matrices

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

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.

Explores subspaces, spectra, and projections in linear algebra, including symmetric matrices and orthogonal projections.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Covers linear systems, diagonal and triangular matrices, and LU factorization.

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