Support Vector Machines: Theory and Optimization

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Covers weak and strong duality in optimization problems, focusing on Lagrange multipliers and KKT conditions.

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

Covers optimization methods without constraints, including gradient and line search in the quadratic case.

Covers semi-definite programming and optimization over positive semidefinite cones.

Explores Gaussian Process-Based Model Predictive Control for HVAC systems using Gaussian processes, Bayesian statistics, and non-linear equations.

Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.

Covers advanced optimization techniques using Lagrange multipliers to find extrema of functions subject to constraints.

Explores Support Vector Machines, maximizing margin for robust classification and the transition to soft SVM for non-linearly separable data.

Explores the application of the representer theorem in kernel methods and its implications in regularized ERM.

Covers optimization without constraints using the gradient method to find the function's minimum.