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Lecture
Latent Factor Analysis: Movie Genre Classification
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Related lectures (27)
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Singular Value Decomposition
Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.
Eigenvalues and Eigenvectors Decomposition
Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.
QR Factorization: Least Squares System Resolution
Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.
Singular Value Decomposition: Orthogonal Vectors and Matrix Decomposition
Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.
Cholesky Factorization: Theory and Algorithm
Explores the Cholesky factorization method for symmetric positive definite matrices.
Singular Value Decomposition
Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.
Spectral Decomposition
Explores spectral and singular value decompositions of matrices.
Singular Value Decomposition
Covers the Singular Value Decomposition (SVD) of a matrix and its applications.
Construction of an Iterative Method
Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.
SVD: Singular Value Decomposition
Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.
