Lectures related to Robust Optimization: Radiation Therapy & Support Vector Machines

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

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

Explores dual cones, generalized inequalities, SDP duality, and KKT conditions in convex optimization.

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

On Convex Optimization covers course organization, mathematical optimization problems, solution concepts, and optimization methods.

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.