Lectures related to Spectral Decomposition of Symmetric Matrices

Covers the diagonalization of symmetric matrices and the spectral theorem.

Explores stationary points, saddle points, symmetric matrices, and orthogonal properties in optimization.

Explores the diagonalization of symmetric matrices using eigenvectors and eigenvalues, emphasizing orthogonality and real eigenvalues.

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

Covers the properties of symmetric matrices, orthogonal matrices, and eigenvalues.

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