Lectures related to Symmetric Matrices and Eigenvalues

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

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

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

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

Explores spectral and singular value decompositions of matrices.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Explains how the inverse of a matrix shares its eigenvalue and how the transposed version has the same set of eigenvalues.