Singular Value Decomposition: Orthogonal Diagonalization

This lecture covers the concept of Singular Value Decomposition (SVD) and the process of orthogonal diagonalization. It starts by explaining the importance of SVD in decomposing matrices and then delves into the steps involved in orthogonal diagonalization, emphasizing the rearrangement of eigenvalues in descending order. The lecture further explores the relationship between SVD and the diagonalization of symmetric matrices. It also addresses scenarios where matrices are not square and provides strategies to handle such cases. Additionally, it discusses the significance of unitary matrices and the role they play in the decomposition process.