TIES5830 Data-driven optimization and decision making (5 cr)
Description
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.
Completion methods
Course assignments, active participation in lectures and discussions, group discussion, and final project.
Assessment details
Assignments, active participation in lectures and discussions, final project. This may vary based on how the course is implemented.
Learning outcomes
Depends on the topics covered in the course (see the description of contents below).
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.