Covers the basics of linear programming, defining corners, extreme points, and feasible solutions within polyhedrons.
Covers methods for solving nonlinear optimization problems, including direct search, Newton-Raphson, and branch and bound.
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.
Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.
