Singular Value Decomposition: Applications and Interpretation

Related lectures (21)

Page 1 of 3

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

Covers the theorems related to orthogonal projection and orthonormal bases.

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

Explores the fundamentals of Singular Value Decomposition, including orthonormal bases and practical applications.

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

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

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

Explains orthogonal families, bases, and projections in vector spaces.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.