Lectures related to Matrix Computations: Eigenvalues and Eigenvectors

Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.

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

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

Explores eigenvalue problems, iterative methods, convergence properties, and applications in computational physics.

Covers matrix eigenvalues, eigenvectors, and their linear independence.

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

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

Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.