Lectures related to Singular Value Decomposition: Image Compression and Applications

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.

Covers matrix rank, determinant, and decomposition in linear algebra.

Discusses eigenvalues, their calculation methods, and their applications in optimization and numerical analysis.

Covers unsupervised learning for movie recommendation using singular value decomposition.

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.