Lectures related to Eigenvalues and Eigenvectors of Markov Chains

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

Covers the concept of symmetric matrices, orthogonal bases, and eigenvectors.

Explores symmetric matrices, diagonalization, and orthogonality properties, emphasizing simplicity and geometric relationships.

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

Explores the diagonalization of symmetric matrices and the importance of Singular Value Decomposition.

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

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

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