First cross-border continuing education courses held in June: “a fantastic opportunity”

In June, two independently bookable Machine Learning advanced training courses developed within the framework of KTUR were successfully held. The participants of the English-language courses in Karlsruhe (introductory course) and Strasbourg (advanced course) gained insights into application-oriented topics of machine learning and artificial intelligence.

In the introductory course, the theoretical basics were applied to real data in practical exercises. Python, Scikit-learn and Kaggle were used. Course leader Prof. Dr. Manfred Strohrmann, Professor of Systems Theory and Dean of the Faculty of Electrical Engineering and Information Technology, evaluates the cross-border offer as “a fantastic opportunity to discover and apply the new world of Machine Learning in an intercultural group”.

KTUR continuing education course
A little rest between the learning blocks - also an aspect of intercultural communication and joint learning (Foto: Julia Durau)

The goal of the advanced course was to develop an understanding of Deep Learning and data visualisation. Theoretical knowledge about components and architectures of neural networks was applied to real data in practical exercises with supervised and unsupervised approaches (Python and Tensorflow).

Creating a cross-border offer in the field of continuing education proved to be a great challenge for the KTUR organisational team under the leadership of the Institute for Scientific Continuing Education at Karlsruhe University of Applied Sciences.

“By developing a cross-border continuing education offer, from conception to implementation, we were able to gain many exciting and enriching insights into the working methods of our partner universities,” says Romina Junk, Managing Director of the Institute for Scientific Continuing Education at Karlsruhe University of Applied Sciences.

Within the framework of KTUR, an approach to the topic of cross-border continuing education could be implemented with the Machine Learning course, which will also be offered in future semesters; further efforts to institutionalise corresponding offers are highly dependent on a basis of trust between all potential participants, which must be continuously strengthened.

Further information can be found here.