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Lecture
Singular Value Decomposition
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Related lectures (46)
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Singular Value Decomposition: Image Compression and Applications
Covers Singular Value Decomposition, focusing on its application in image compression and data representation.
Singular Value Decomposition
Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.
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Covers the basics of linear algebra, including matrix operations and singular value decomposition.
Linear Algebra: Singular Value Decomposition
Delves into singular value decomposition and its applications in linear algebra.
SVD: Singular Value Decomposition
Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.
Singular Value Decomposition: Example
MOOC: Linear Algebra (Part 3)
Explains the step-by-step process of finding the singular value decomposition of a matrix.
Clustering: K-means & LDA
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Unsupervised Learning: Principal Component Analysis
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Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.
Unsupervised Learning: Clustering & Dimensionality Reduction
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Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
Unsupervised Learning: Movie Recommendation
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Covers unsupervised learning for movie recommendation using singular value decomposition.
Singular Value Decomposition (SVD)
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Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.
Unsupervised Learning: PCA & K-means
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Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Spectral Decomposition
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Explores spectral and singular value decompositions of matrices.
Eigenvalues and Eigenvectors Decomposition
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Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.
Unsupervised Learning: Dimensionality Reduction and Clustering
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Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.
Matrices and Quadratic Forms: Key Concepts in Linear Algebra
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Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.
Convex Optimization: Linear Algebra Review
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Provides a review of linear algebra concepts crucial for convex optimization, covering topics such as vector norms, eigenvalues, and positive semidefinite matrices.
Linear Algebra Review: Convex Optimization
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Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.
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