Singular Value Decomposition: Fundamentals and Applications

Related lectures (24)

Page 2 of 3

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Explores landmarks, coordinates, vectors, coplanarity, Cartesian equations, and geometric rules in space.

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

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

Covers the Singular Value Decomposition theorem and its applications in practice.

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

Covers the concept of 3D vector space, scalar product, bases, orthogonality, and projections.

Explains linear independence, basis, and matrix rank with examples and exercises.

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