Maschinelles Lernen
At the University of Wuppertal (BUW), machine learning occupies a key position and represents a dynamic research and teaching focus within computer science. Machine learning is at the center of today's technological progress and dominates the innovation landscape in many areas. At BUW, we strive to make advances through machine learning that enable us, among other things, to gain deep insights into complex data and develop predictive models of high accuracy. Our research initiatives range from the development of new algorithms to the implementation of autonomous systems that are able to learn from data, adapt and make autonomous decisions.
The collaboration between the Faculty of Mathematics and Natural Sciences and the Faculty of Electrical, Information and Media Engineering promotes a multi-layered approach that ranges from theoretical foundations to practical applications. This cross-faculty collaboration enables the development and advancement of innovative solutions for the data-driven challenges of our time.
Areas of application
Within machine learning, we focus on various key areas:
In this key area, work is being carried out on the development of methods for evaluating and communicating uncertainties in the predictions of machine learning models. The aim is to increase the reliability and transparency of artificial intelligence by providing users with insights into the limits and confidence level of model predictions. This is particularly important in critical fields of application where decisions based on AI recommendations can have far-reaching consequences.
In this key area, methods are being developed to use machine learning to help solve problems in the natural sciences, such as chemistry, physics, etc. An important challenge lies in the utilization of process knowledge from the respective discipline in new methods. In addition, the construction of models with high accuracy despite low data availability or high data costs is of great interest. New approaches often emerge from the combination of methods from scientific computing and machine learning.
The focus on “Machine Learning” is completed by the Interdisciplinary Center for Machine Learning and Data Analytics (IZMD), which acts as a cross-faculty institution. The IZMD pools expertise in machine learning and data analytics in order to promote both scientific research in these fields and the transfer of these technologies to industry and society. With the “Bergisch Innovation Platform for Artificial Intelligence (BIT)” as its transfer arm, the center supports the application of machine learning in the regional economy and promotes dialogue between science, industry and society.
Chairs and research groups
The following computer science chairs and research groups are active in the “Machine Learning” focus area:
General electrical engineering and theoretical communications engineering (Prof. Anton Kummert)
Applied mathematics (Prof. Michael Günther und Prof. Matthias Ehrhardt)
Automation technology / Computer science (Prof. Dietmar Tutsch)
Designing trustworthy artificial intelligence (Prof. Hendrik Heuer)
Software for Data-Intensive Applications (Prof. Peter Zaspel)
Technologies and management of digital transformation (Prof. Tobias Meisen)
and in cast:
Data Analytics
Massive Data Processing
