Lectures related to Eigenvalues and Eigenvectors Decomposition

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

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.

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

Explores spectral and singular value decompositions of matrices.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

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

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

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.