Bachelor Courses
Bachelor Courses
In this course, we cover the framework of computer programming and its concepts. We dive into step-by-step programming and gradually go more into detail with the use of Python programming language and the concept of computational thinking.
This course introduces various modelling and solution methods in operations research when parameters of optimization problems are deterministic. In addition to investigating the mathematical principles and the algorithms, students are expected to model and solve real-world applications in Operations Research. The course covers topics related to simplex method, duality, network optimization, integer optimization and branch & bound algorithms.
This course gives an introduction to the theories behind operations research when the application of deterministic models fall short, for example when randomness and uncertainty play a significant role. The course covers topics from sensitivity analysis, stochastic optimization, robust optimization, metaheuristics and markov decision processes.
In recent years, there has been a rising demand for transparent and explainable machine learning (ML) models. A large stream of works focuses on algorithmic methods to derive so called counterfactual explanations (CE). In this lecture, we will introduce the main concept and show how these explanations can be efficiently calculated for a large class of ML models by gradient methods or constraint-learning techniques. Afterwards, we will show how robust optimization methods can be used to calculate regions of CEs which improve the flexibility for the user and the robustness of the CEs. Finally, we will show how the concept of CEs can be used to calculate useful explanations for linear optimization problems.
Joint supervision with Bol