Scientific Machine Learning (SciML) is a growing field in which methods and techniques from machine learning and scientific computing coalesce. SciML spans across the scientific domains of the CDS, and it is the goal of the SciML Lab to bring together CDS scientists to share their expertise, collaborate on new projects, and foster the research on scientific machine learning.
The name Scientific Machine Learning was coined in January 2018 at a US Department of Energy (DOE) Basic Research Needs workshop; see www.osti.gov/biblio/1478744.
Selection of courses offered in SS 2025
[Lecture] Introduction to Data Science and Machine Learning (Prof. Dr. W. Ketter)
[Lecture] Mathematics of Data Science - An Introduction (Prof. Dr. A. Klawonn)
[Lecture] Artificial Intelligence and Information Management (Prof. Dr. D. Schoder)
[Lecture] High Performance Computing for Machine Learning (Dr. J. Weber)
[Seminar] Limitations of Large Language Models (Prof. Dr. A. Bojchevski)
[Seminar] Machine Learning Methods to Predict Individual Differences With Python (Jun.-Prof. Dr. B. Gagl)
[Seminar] Periodical Solutions in Mathematical Models for Neural Nets (PD Dr. F. Giannakopoulos)
[Seminar] Current Trends in Visualization (Prof. Dr.-Ing. T. von Landesberger)
[Seminar] Methods of Mathematical Modeling in Life Sciences (PD Dr. T. Mrziglod)
[Seminar] Mathematical Foundations of the Natural Language Processing (Dr. Z. Nikolić)
[Seminar] Seminar for Teachers at Grammar and Comprehensive Schools: AI Algorithms in Teaching (Prof. Dr. U. Trottenberg & Dr. R. Wienands)