Matrix Diagonalization: Spectral Theorem

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Explores spectral and singular value decompositions of matrices.

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

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

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Explores the diagonalizability of symmetric matrices and their eigenvectors in an orthonormal basis.

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

Covers the concept of symmetric matrices, orthogonal bases, and eigenvectors.

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.

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