Lectures related to Eigenvalues and Eigenvectors: Understanding Matrix Transformations

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

Covers eigenvalues, eigenvectors, and characteristic polynomials in matrix transformations.

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

Explains eigenvalues and eigenvectors in linear algebra with practical examples and properties of matrix transformations.

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

Covers the criteria for diagonalizing a matrix and provides illustrative examples.

Explores the reduction of a linear transformation with a single real eigenvalue.

Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.