Lectures related to Eigenvalues and Eigenvectors

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

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

Explores eigenvalues, matrix trace, and similarity, highlighting their significance in matrix properties.

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

Covers the second criterion of diagonalization and similar matrices in linear applications.

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Explores orthogonality, eigenvalues, and diagonalization in linear algebra, focusing on finding orthogonal bases and diagonalizing matrices.

Explores matrices, eigenvalues, and diagonalizability, including invertibility and vector spaces.