Matrix Decomposition: Triangular and Spectral

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Covers linear systems, diagonal and triangular matrices, and LU factorization.