10.12.2024

Teaser image to MCML Featured in Bildung + Schule Digital Magazine

MCML Featured in Bildung + Schule Digital Magazine

KITrans: Learning and Understanding AI

The Munich Center for Machine Learning (MCML) is highlighted in the latest issue of Bildung+ Schule Digital Magazine for its role in the innovative KITrans project.

KITrans is an innovative educational project bringing accessible AI learning formats to classrooms. Together with Prof. Uta Hauck-Thum from LMU Munich and Junge Tüftler, the project introduces students and teachers to AI's potential, challenges, and responsible use through interactive workshops and co-creative formats. Participants learn to develop and adapt AI models, gaining practical insights into algorithms and data analysis.

10.12.2024


Subscribe to RSS News feed

Related

Link to Gitta Kutyniok Appears on ARD Audiothek

01.09.2025

Gitta Kutyniok Appears on ARD Audiothek

MCML PI Gitta Kutyniok featured on ARD Audiothek, discussing climate-neutral AI and sustainable data centers.

Link to Björn Ommer featured on IT-BUSINESS podcast

21.08.2025

Björn Ommer Featured on IT-BUSINESS Podcast

MCML PI Björn Ommer discussed the state and opportunities of generative AI in Germany on the IT-BUSINESS podcast.

Link to MCML PI Björn Schuller Featured in ARD Documentary on AI and Depression Therapy

05.08.2025

MCML PI Björn Schuller Featured in ARD Documentary on AI and Depression Therapy

MCML PI Björn Schuller discusses the use of AI as a digital therapist in ARD’s documentary “Depression - How to Get Out of It?”

Link to Stephan Günnemann featured in BR24

01.08.2025

Stephan Günnemann Featured in BR24

MCML PI Stephan Günnemann was featured in BR24 on the future of AI agents and their role in software, law, and corporate decision-making.

Link to AI research by Daniel Rückert improves medical imaging and data privacy

29.07.2025

AI Research by Daniel Rückert Improves Medical Imaging and Data Privacy

Daniel Rückert develops AI for faster medical imaging and secure data use through federated learning and privacy-preserving methods.