Mastering complexity to navigate a complex world

To understand the complex processes in the world around us and to interpret the vast amount of complex datasets available today  we need a set  of scientific methods. This website links to courses that will provide the basic tools and concepts to model and quantitatively analyze complex systems or data sets and will enable the students to apply this knowledge to their specific scientific field or in an interdisciplinary context.

Modeling complex dynamical systems

To understand the dynamics of complex systems it is not enough to understand their parts. It is important to understand the interactions and the new phenomena resulting from the interplay between the pieces. Such systems can be found everywhere in natural and social sciences or in technology. Examples are the climate system, interacting species, the dynamics of economic systems or the power grid to name just a few.

Introduction to nonlinear dynamics and self-organization 

Fermi questions and orders of magnitude -- Science on the back of the envelope 

Hands-on training nonlinear modeling

Tutorials on continuation

Data driven approaches

The rapid development of technological possibilities for the generation, networking and processing of data continuously opens up new possibilities for the use of data driven methods in research, development and application. Here several courses ranging vom basuc data analysis to KI are offered.

Introduction to Bayesian statistics

A short Course on Causal Inference 

Machine learning and artificial intelligence 

The InterKIWWU programm offers a university-wide range of courses on machine learning (ML) and artificial intelligence (AI) with basic modules, advanced modules on applications in various fields of science and advanced modules on in-depth aspects. Additional expanding modules give space for reflections on AI with regard to sustainability and ethics, and consider the transfer to business.

InterKIWWU - teaching program on ML and KI