Lectures related to Spectral Decomposition and SVD

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

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

Explores spectral and singular value decompositions of matrices.

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

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

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

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

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

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

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

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

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

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

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

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

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

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

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

Explores the Jacobi method and diagonalization techniques, including similarity transformation, power methods, and QR decomposition.

Covers linear systems, diagonal and triangular matrices, and LU factorization.