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Related lectures (28)

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Distributions and Derivatives

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Graph Sketching: Connected Components

Covers the concept of graph sketching with a focus on connected components.

Sparsest Cut: Bourgain's Theorem

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Sparsest Cut: ARV Theorem

Covers the proof of the Bourgain's ARV Theorem, focusing on the finite set of points in a semi-metric space and the application of the ARV algorithm to find the sparsest cut in a graph.

Multivariate Statistics: Normal Distribution

Covers the multivariate normal distribution, properties, and sampling methods.

Exponential Family: Maximum Entropy Distributions

Covers exponential families and maximum entropy distributions under moment constraints.

Information Measures

Covers variational representation and information measures such as entropy and mutual information.

Linear Similarities and Similitudes

Explores linear similarities and similitudes, demonstrating their properties and applications in geometry.

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.

Sparsest Cut: Leighton-Rao Algorithm

Covers the Leighton-Rao algorithm for finding the sparsest cut in a graph, focusing on its steps and theoretical foundations.