Singular Value Decomposition: Image Compression and Applications

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Covers the norm of a matrix, operator, singular values, and unitary matrices in linear algebra.

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Singular Value Decomposition, Pseudoinverse

Covers Singular Value Decomposition and Pseudoinverse, explaining their applications in data compression and linear systems.

Principal Component Analysis: Applications and Limitations

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

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Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA

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Singular Value Decomposition: Applications and Interpretation

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

Convex Optimization: Linear Algebra Review

Provides a review of linear algebra concepts crucial for convex optimization, covering topics such as vector norms, eigenvalues, and positive semidefinite matrices.

Singular Value Decomposition

Introduces Singular Value Decomposition (SVD) in linear algebra, covering matrix factorization and properties with practical examples.

Singular Value Decomposition: Theory and Applications

Explores Singular Value Decomposition theory, properties, uniqueness, matrix approximation, and dimensionality reduction applications.