Lectures related to Orthogonal Diagonalization: Symmetric Matrices

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

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

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

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

Explores the diagonalizability of symmetric matrices and their eigenvectors in an orthonormal basis.

Explores normal equations, pseudo-solutions, unique solutions, and symmetric matrices in linear algebra.