Content: In this seminar, we will discuss current and classical research papers which describe machine learning methods for applications in the natural sciences. From a methodological perspective, a particular focus will be on `simulation-based inference approaches’, as these provide a bridge between data-driven machine learning methods, and theory-driven scientific modelling, as well as on latent-variable models for inferring dynamical systems from data.
Objectives: Students are able to read and reflect upon current research papers in this research area. They can critically assess the contributions of such a paper. They can present current research results to other students and researchers and can lead research discussions. They can summarize and evaluate the results of a paper in form of a written research report.
Time: Tuesday, 11:30-1pm
Location: Online, zoom link will be shared with participants
Registration: You can register via Ilias.
Work load: 90h
Class time: 30h/ 2 CH
Self Study 60 h
Duration: 1 semester
Language of Instruction English
Type of Exam: Oral presentation, written report
Requirement for participation: Knowledge in probabilistic machine learning
Lecturer: Macke, Lappalainen, Deistler, Schulz
Literature: Will be announced in the first meeting