TIES5830 Data-driven optimization and decision making (5 cr)
Students will learn the basics of online and offline data-driven multiobjective optimization, data-preprocessing, various types of surrogate model selection, model management techniques, interactive data-driven optimization and decision support tools.
Course assignment, group discussion and final project
Assignment and active participation in lectures and discussions. This may vary based on how the course is implemented.
Description of prerequisites
Programming skills (preferably Python but not mandatory), basics of optimization (recommended) and machine learning, numerical methods. Completing the courses TIES598 (Nonlinear Multiobjective Optimization) and TIES451 (Selected Topics in Soft Computing) is beneficial.