Machine Learning: Course and Workshop
building 04.8, room 142/143
FZ Jülich
The Palestinian-German Science Bridge and Helmholtz AI offer a basic course and a workshop on machine learning for PIs and supervisors/senior scientists in both natural and computer science. Potential applications of machine learning and deep learning in natural science are numerous; they include detector development, data analysis techniques, and even physical modelling. The event consists of an introductory and an advanced module, which you can book independently if you are not a core participant. The content beyond the basic module with an introduction to deep learning will be decided based on a survey conducted among the participants before the event.
The highly interactive, hands-on introductory module will equip the participants with basic tools to implement and use machine learning methods. All participants are expected to contribute to the course actively. A Python course that introduces participants that are not familiar using basic Python, pandas, and Matplotlib to the necessary basics and packages will be offered prior to the machine learning modules as a virtual course.
Modules:
- deeplearning in 540 minutes: In the first module on 28-30 August, the machine learning course will enable participants to embark on clustering, classification, and regression projects using “scikit-learn” or “keras”. (If you are already familiar with implementing machine learning in Python and do not belong to the core participants of the workshop, you can skip this module.)
- Advanced machine learning: In the second module on 31 August and 1 September, ample opportunities will be provided for working on advanced topics and current research projects, which will be selected considering the group's needs. Advanced topics may include--for example--reinforcement learning, genetic algorithms, computer vision, how to determine the robustness and reliability of a model, strategies if the basic algorithm fails, and physics-informed machine learning. In addition to advanced machine-learning topics, the advanced content may include an extended mathematical introduction to theoretical concepts, enriching the workshop's benefits and outcome. (If you want to learn the basics of machine learning only and do not belong to the core participants of the workshop, you can skip this module.)
Prerequisites: Knowledge of basic Python, Pandas, and Matplotlib is required for successful participation. It will be provided to those that are not yet familiar in our preparatory PGSB Python course that will be delivered online (Registration: https://events.hifis.net/e/pgsb_python_23).
Participants: Please note that this course is aimed at the PI/supervisor/IT/staff scientist/postdoc level and is not intended for PhD students. A first selection of participants will take place soon after 6 August 2023.
Timetable: The timing may be adjusted to meet the needs of the participants.