Eigenvalues and Eigenvectors Decomposition

Related lectures (25)

Page 2 of 3

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Explores the diagonalization of symmetric matrices and the importance of Singular Value Decomposition.

Explores subspaces, spectra, and projections in linear algebra, including symmetric matrices and orthogonal projections.

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

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

Covers the calculation of eigenvalues and eigenvectors, emphasizing their significance and applications.

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

Explores diagonalization in symmetric matrices, emphasizing orthogonality and orthonormal bases.

Explores normal equations, pseudo-solutions, unique solutions, and symmetric matrices in linear algebra.

Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.