Singular Value Decomposition: Orthogonal Vectors and Matrix Decomposition

This lecture covers the concept of Singular Value Decomposition (SVD) in linear algebra, focusing on the decomposition of a matrix into orthogonal vectors and the importance of singular values. It explains the process of constructing an orthonormal basis, determining singular values, and obtaining the SVD of a square matrix. The lecture also discusses the significance of unitary and orthogonal vectors in the decomposition process, along with practical examples and calculations.