Lab Deep Learning for the Physical Sciences (MA-INF 4244, 9CP)
Description
This labs offers an introduction to machine learning and deep learning methods applied to topics of the physical sciences. This semesters topic is astrophysics.
The first weeks of the lab will include several exercise sheets to introduce astrophysical data and how common machine learning algorithms can be applied to them. The last few weeks will be a project where students can extend the ideas from the exercise on a larger version of the data.
Requirements
- There are no formal course requirements.
- All projects will be implemented in Python and prior experience is recommended. There will be an optional introduction to Python but no in-depth explanations.
Course Details
- Lecturers: Prof. Dr. Zorah Lähner, Dr. Alexander Rüttgers (DLR)
- Curriculum: Master Computer Science
- Registration: Basis
- Material: eCampus
- Effort: 4SWS seminar, 9CP
- Examination: Lab project, presentation and report
- Course time: Wednesdays 10-12 c.t.
- Room: Friedrich-Hirzebruch-Allee 6-8, Room 3.035b
Participation
The first session on April 15th at 10:15 includes an introduction and overview of the course material. The weekly session will alternate between presentation of new material and in-person coding sessions where you can get help with the exercises. The last few weeks are reserved for a final project.
The course is open for astrophysics students but they cannot receive credits. If participation is too high, computer science students will have priority.