This course introduces students to algorithms and techniques for optimization and nonlinear programming problems. Students will learn important numerical optimization methods such as the gradient descent, the Newton’s method, the quasi-Newton’s methods for unconstrained optimization, and the methods for constrained optimization. The classic methods for machine learning such as the stochastic gradient descent and its acceleration techniques, will be covered as well.

Note: To pass the course, apart from obtaining a minimum of 40% in the overall mark, a student must also obtain a minimum mark of 30% in both continuous assessment and examination components.