Singular Value Decomposition

This lecture covers the concept of Singular Value Decomposition (SVD) in linear algebra. SVD is a factorization method that decomposes a matrix into singular vectors and singular values. The lecture explains how to find the non-zero singular values and the orthogonal matrices that form the SVD. It also discusses the properties and applications of SVD, such as matrix diagonalization and rank determination. The instructor demonstrates the theorem related to SVD and provides examples of matrices with rank r. Additionally, the lecture explores the symmetrical nature of matrices and their decomposition using SVD.