Lectures related to Eigendecomposition of a matrix

Explores spectral and singular value decompositions of matrices.

Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.

Explores eigenvalues and eigenvectors of Markov chains, focusing on convergence rates and matrix properties.

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

Explores algebraic and geometric multiplicities of eigenvalues in linear algebra.

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Explores the application of matrices and eigendecompositions in networks.

Covers matrix eigenvalues, eigenvectors, and their linear independence.