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).
Contact: Katrin Schmietendorf
Introduction to Nonlinear Dynamics and Self-Organization
Nonlinear dynamics occur in a wide range of natural, technological, and social systems. They govern mechanical, hydrodynamic, and chemical processes as well as biological rhythms, neuronal activity, ecological systems, and collective social phenomena. Even simple models can give rise to surprising dynamics, ranging from extreme sensitivity to initial conditions and the coexistence of multiple stable states to sudden qualitative changes in dynamical behavior.
The study of nonlinear systems is one of the core areas of modern physics and at the same time provides an important foundation for understanding nonlinear and collective phenomena across many other scientific disciplines. Despite the wide range of specific applications, many of these systems exhibit similar behaviors and follow universal principles that can be described using a common mathematical framework.
This lecture provides an introduction to the fundamental concepts and methods of nonlinear dynamics. Topics include phase spaces, attractors, stability and multistability, bifurcations, oscillations, critical transitions, chaos, and stochastically driven systems. Building on these foundations, selected phenomena of self-organization in complex systems are discussed, including synchronization and spatial pattern formation. Examples from physics, biology, chemistry, and the neurosciences and social sciences illustrate the multidisciplinary relevance of nonlinear and complex systems.
The accompanying exercises are designed to deepen the lecture material through selected examples that are worked on independently by the students and subsequently presented and discussed during the in-person sessions.
The next lecture will take place in the winter semester 2026/2027. For further information see HIS-LSF.
Contact: Katrin Schmietendorf
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