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This lecture covers the theoretical foundations of Singular Value Decomposition (SVD), explaining the decomposition of a matrix into singular values and vectors. It discusses the properties of symmetric matrices, eigenvalues, and eigenvectors, illustrating the SVD process step by step. The lecture also delves into the significance of SVD in linear algebra, emphasizing the exceptional cases and practical applications.