Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Covers optimization methods, focusing on gradient methods and line search techniques.
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.
