Related lectures (14)
Low Rank Approximations
Explores low rank approximations, spectral theorems, and orthogonality in matrices.
Dimensionality Reduction
Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA
Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.
Unsupervised Learning: Principal Component Analysis
Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.
Principal Components Analysis
Covers Principal Components Analysis, a technique for dimensionality reduction and gene type analysis.
Singular Value Decomposition
Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.
Singular Value Decomposition: Fundamentals
Covers the fundamentals of Singular Value Decomposition, including properties, applications, and error measurement.
Singular Value Decomposition
Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

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