Lectures related to Singular Value Decomposition: Orthogonal Vectors and Matrix Decomposition

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.

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

Covers orthogonal bases, Gram-Schmidt method, linear independence, and orthonormal matrices in vector spaces.

Delves into singular value decomposition and its applications in linear algebra.

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