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Related lectures (25)

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Covers the properties and applications of orthogonal matrices.

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Explores the spectral decomposition of symmetric matrices, including diagonalization and orthogonal basis change matrices.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Explores orthogonality between vectors and subspaces, demonstrating practical implications in matrix operations.

Covers the decomposition of matrices into triangular blocks and spectral decomposition.

Explains orthogonal matrices, Gram-Schmidt process, and best vector approximation in subspaces.

Explains the Principal Axes Theorem for symmetric matrices and quadratic forms, showing the existence of orthogonal matrices for diagonalization.

Introduces orthogonal and symmetric matrices in linear algebra with practical examples.

Delves into singular value decomposition and its applications in linear algebra.