Singular Value Decomposition

Related lectures (24)

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Covers the theorems related to orthogonal projection and orthonormal bases.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Covers the Factorisation QR theorem and the Gram-Schmidt method for orthonormal bases.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Introduces Singular Value Decomposition (SVD) in linear algebra, covering matrix factorization and properties with practical examples.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

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

Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.

Explores the equivalence between different properties of linear transformations represented by matrices and various matrix operations.