Current courses

• Winter term 2025/26: Seminar Machine Learning for Scientific Discovery

Topic: Diffusion models. See Ilias for mored details.

Regular Research Seminar

In this weekly seminar we present and discuss current research in the area of Machine Learning in Science. This includes simulation-based inference, computational neuroscience and various other topics. Students (Bachelor or Master), PhD students and PostDocs are invited to join us.

Place and time: The seminar takes place in MvL1 (3rd floor/level 5, big seminar room) every Tuesday from 11:00 to 12:30.  If you like to participate, please write an e-mail to Cornelius Schröder who will provide you with further details. The schedule of the seminar can be found here.

Past courses

• Summer term 2025: Seminar Machine Learning for Scientific Discovery

This year we explore current advancements in SBI by studying recent research papers. The seminar begins with two sessions: an introduction to critical reading of scientific papers, followed by an overview of SBI, its core concepts, and applications. We will then study current research papers in the field of SBI.

• Winter term 2024/25: Lecture Data Literacy

• Summer term 2024: Lecture Probabilistic Machine Learning

• Summer term 2023: Seminar Machine Learning for Scientific Discovery

The seminar is for students who want to specialize in machine and deep learning methods to gain scientific insights. This year we focus on the whole-nervous-system understanding and the detailed neural circuit simulation of the fly (Drosophila), one of the most comprehensively understood organisms on the planet.

• Winter term 2022/23: Data Literacy

This course equips students with concepts and tools for working with (large) data. Based on practical examples, we discuss frequently encountered problems alongside best practices. We cover basic statistical notions and problems of bias, testing and experimental design, and employ methods of machine learning and statistical data analysis to put these ideas in practice. We also discuss best practices for scientific data presentation and documentation and explore ethical and technical considerations in the context of fairness and transparency. The exercises will have a focus on practical programming.

• Summer term 2022: Probabilistic Machine Learning

The course provides an introduction to core concepts of machine learning from the probabilistic perspective.