From finance and health to science and engineering, optimisation has many applications and is at the heart of several modern technologies such as machine learning for big data and deep neural networks. This course develops the student’s ability to understand and apply the fundamental analytical, computational and statistical techniques for optimising deterministic and stochastic problems in practice.

The first part of the course deals with deterministic optimisation problems where all parameters are known, including linear and nonlinear programs. In practice, however, we often encounter systems, for which parameters are uncertain. The focus of the second part of the course is on methods for optimising stochastic systems, particularly where the dynamic of the system is governed by a Markov chain, such as in supply chains and queueing networks.

The written assignments give students the opportunity to apply the concepts they learn in lectures and labs to a number of theoretical and computational problems. The topics align with the course content covered by that stage of the semester.

The project gives students the opportunity to experience applying the concepts they learn in the course to a more applied problem as a teamwork. The project output involves a written report and verbal presentation.