Lectures related to Singular Value Decomposition: Fundamentals and Applications

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Explores linear combinations of vectors and matrices in Rn, demonstrating geometric interpretations and matrix operations.