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Related lectures (32)

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Geodesic Convexity: Theory and Applications

Explores geodesic convexity in metric spaces and its applications, discussing properties and the stability of inequalities.

Dispersion Models: Understanding Axial Dispersion Coefficient

Explores dispersion models and the axial dispersion coefficient in fluid systems.

Learning the Kernel: Convex Optimization

Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.

Random-Subcube Model

Introduces the Random-Subcube Model (RSM) for constraint satisfaction problems, exploring its structure, phase transitions, and variable freezing.

KKT and Convex Optimization

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Convex Optimization: Gradient Descent

Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Orchestration Graph Concepts

Explains the use of operators for transferring data between activities in an orchestration graph.

Markov Chains: Transition Probabilities

Explores Markov chains, transition matrices, distribution, and random walks.

Optimal Transport: Kantorovich Duality

Covers optimal transport and Kantorovich duality in real-life distribution problems.