INTRODUCTION
This course gives students insights into the recent developments in a fast-growing research line: the integration of techniques from machine learning (ML) and optimization (OPT). The lectures cover different aspects of the integration of ML and optimization. Example topics include:
- Machine learning, including deep learning and reinforcement learning, to solve combinatorial optimization problems; black-box optimization; neural combinatorial optimization; and handling uncertainties of prediction models for decision-making
- Using optimization algorithms for the development of transparent ML models, such as learning optimal decision trees
- Computing explanations for ML model via techniques developed for optimization or constraint reasoning systems
Lectures are complemented by lab sessions with hands-on exercises during the course.
The two-day course will be held in Utrecht in person. To participate, students should have basic knowledge on machine learning and some basic knowledge on optimization (i.e., mathematical programming, or (meta-)heuristics).
DATE:
Tuesday 11 and Wednesday 12 June 2024
LOCATION:
SCIENTIFIC DIRECTORS:
Dr Zaharah Bukhsh, TU/e
Dr Jie Gao, TU Delft
Dr Neil Yorke-Smith, TU Delft
Dr Yingqian Zhang, TU/e
PROGRAMME
The full details will be published nearer the time.
REGISTRATION
Registration for this course is now open.
