Singular Value Decomposition: Theory and Applications

This lecture covers the theory and applications of Singular Value Decomposition (SVD) in linear algebra. It explains the concept of singular values, their calculation, and the decomposition of a matrix into orthogonal matrices U and V, along with a diagonal matrix Σ. The lecture also discusses the properties of SVD, such as the relationship between ATA and AAT, and practical examples of calculating singular values. The instructor emphasizes the importance of SVD in various fields and provides step-by-step explanations of the decomposition process.