Support Vector Machines: Theory and Optimization

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Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Duality: Duality in Linear Optimization

Covers the concept of linear optimization and the duality relationship between primal and dual problems.

Optimization: Constrained Volume Problems

Explores constrained volume problems using Lagrange multipliers to find extrema under constraints in various examples.

Energy Systems Optimization

Explores energy systems modeling, optimization, and cost analysis for efficient operations.

Linear Models for Classification

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Optimization Principles

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Linear Programming Duality

Explores the concept of duality in linear programming and its practical implications in optimization.

Linear Optimization: Directional Derivatives

Explores directional derivatives in linear optimization and their impact on objective functions.

Primal-dual Optimization III: Lagrangian Gradient Methods

Explores primal-dual optimization methods, emphasizing Lagrangian gradient techniques and their applications in data optimization.

Transporters: a proxy for parallel transport

Explores transporters as a practical alternative to parallel transport, discussing minimal requirements, examples with matrices, pragmatic choices, and optimization algorithms.