Dates: 16 - 19 October 2023, 9 am - 12:30 pm
Venue: Center for Free-Electron Laser Science, SR II and III
Lecturer: Prof. Dr. Gregor Kasieczka, Universität Hamburg
Outline/abstract:
Motivated by the large volume and high complexity of experimental data and mathematical structures, fundamental physics research has a long tradition of employing state of the art computing and analysis techniques. Recent progress in machine learning and artificial intelligence have further pushed this trend, and these approaches are now ubiquitous in our field. This series of lectures will first introduce basic concepts of machine learning with a focus on deep neural networks. We will review relevant architectures for physical data and discuss key techniques including both supervised tasks as well as generative modelling. Finally, we explore current applications to particle physics.
