Support Vector Machines: Dual Formulation for Hard Margin

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Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers the derivation of Linear Support Vector Machine (SVM) and the Karush-Kuhn-Tucker (KKT) conditions.

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Covers exercises on optimal decision making, including minimizing costs and optimizing transportation networks.

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

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

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.