Jordan Form: Decomposition and Properties

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Explores the unique decomposition of matrices into diagonalizable and nilpotent parts, showcasing their properties and applications.

Explores the Jordan normal form and its applications in linear algebra, focusing on diagonalization and cyclic bases.

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

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

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

Explores the Cholesky factorization method for symmetric positive definite matrices.

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Covers the Singular Value Decomposition (SVD) of a matrix and its applications.

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

Explores latent factor analysis for movie genre classification based on male versus female leads.