Lectures related to Singular Values, Fundamental Theorem

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

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

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

Explores the application of linear algebra in quantum mechanics, emphasizing vector spaces, Hilbert spaces, and the spectral theorem.

Explores diagonalization in symmetric matrices, emphasizing orthogonality and orthonormal bases.