Singular Value Decomposition: Fundamentals and Applications

This lecture covers the fundamentals of Singular Value Decomposition (SVD), including the four fundamental subspaces, SVD of a square matrix, orthonormal bases, and linear combinations of vectors. The lecture also delves into the importance of orthogonal matrices, the calculation of singular values, and the application of SVD in determining an orthonormal basis. Additionally, it explores the process of constructing an orthonormal basis, the significance of singular values, and the practical implications of SVD in real-world scenarios.