Lectures related to Eigenvalues and Eigenvectors

Explores eigenvalues and eigenvectors in matrix transformations, essential for understanding mathematical and real-world systems.

Covers eigenvalues, eigenvectors, and their applications in linear algebra.

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

Explores eigenvalues, eigenvectors, and matrix diagonalization with examples and proofs.

Covers eigenvalues, eigenvectors, characteristic polynomial, and geometric multiplicities in linear transformations.

Covers the spectral decomposition of matrices and change of basis applications.

Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.

Explains the characteristic polynomial of a matrix and its importance in linear algebra.