The lectures listed here form part of the CDSC's permanent course offerings and are regularly available.
You can find the courses offered this semester in the overview of all current courses.
Introduction to Bayesian statistics
Bayesian statistics offers a unifying framework for addressing problems in data analysis and serves as a theoretical foundation for many methods in machine learning. The aim of the course is to provide an introductory overview of the subject through examples from various scientific disciplines, everyday situations, and current applications in machine learning. Building on the theoretical foundations, the course covers topics such as parameter estimation, model comparison, hypothesis testing, causal inference, and experimental design, complemented by their numerical implementation using modern Monte Carlo methods.
Further information about the lecture in summer term 2025 can be found on the LearnWeb
Contact: Oliver Kamps
Interdisciplinary Introduction to Complex Networks
Complex networks are a modern, thematically diverse and highly interdisciplinary field of research. The aim of the lecture is to provide an introduction to the universal toolbox of network theory. It provides concepts and methods for describing and analysing complex networks, for understanding phenomena that occur in them, and for optimising their functioning and resilience. The lecture will present numerous application examples from different disciplines (e.g. physics, chemistry, biology, neuroscience, medicine, economics and sociology), which will be complemented by current topics (e.g. the propagation of disinformation, bicycle networks, climate networks, the spread of epidemics).
The course is currently being developed with QV funding from the University and will be offered for the first time in the 2026 summer term.
Contact: Katrin Schmietendorf
Introduction to Nonlinear Dynamics and Self-Organization
Nonlinear effects and collective phenomena arising from the interaction of many components can be observed across a wide range of scientific disciplines, including physics, chemistry, biology, medicine, engineering, social sciences, and psychology. These include abrupt transitions between states with completely different properties, multistability, and the self-organized emergence of spatial, temporal, and spatiotemporal order and structures. Using examples from various scientific fields, the lecture introduces the modelling of such phenomena and explores the underlying common principles.
Further information: The next lecture will take place in the winter semester 2026/2027.
Contact: Oliver Kamps
Introduction to Machine Learning
This interdisciplinary introductory lecture is aimed at Bachelor’s and Master’s students from various disciplines with diverse prior knowledge. The lecture provides an overview of the fundamental concepts and algorithms of machine learning and explains them using basic application examples.
Further information: The next lecture will take place as a block course during the lecture-free period in the summer semester 2026.
Contact: Oliver Kamps