Munich Center for Machine Learning launched
Machine learning and artificial intelligence are key technologies for the digital economy and society. Leibniz Prize winner Prof. Daniel Cremers from the Technical University of Munich (TUM) is one of the coordinators of a new competence center dedicated to these topics. The Munich Center for Machine Learning (MCML), which is funded by the Federal Ministry of Research, will in the future be connecting key areas of expertise from data science, computer science and statistics.
With advances in computing power and new algorithms, computers can intelligently link data, pinpoint relationships, draw conclusions, and make predictions. This forms the basis for new diagnostic tools in medicine, or for self-controlling systems in logistics and industrial manufacturing. In the Munich region, numerous outstanding researchers are conducting research in the field of Artificial Intelligence (AI). In the current 2018 “Times Higher Education Ranking”, the TUM occupies the sixth place in AI research worldwide. With the establishment of the Munich School of Robotics and Machine Intelligence (MSRM), which combines research on robotics and machine intelligence under one roof, TUM set new standards in 2017 for interdisciplinary research in this field. Director Prof. Sami Haddadin was awarded the Leibniz Prize in 2019 and Prof. Daniel Cremers was awarded the Leibniz Prize in 2016.
Daniel Cremers is an expert in deep-learning methods and neural networks in the field of computer vision and a co-coordinator of the newly established competence center, the Munich Center for Machine Learning (MCML). At the MCML, 15 teams from the fields of data science, computer science and statistics from LMU and TUM have come together to further advance the methods of machine learning and pattern recognition. The new facility is managed by Prof. Thomas Seidl (LMU).
The new Competence Center is dedicated to the processing of various data types. Weather data, stock prices, voice recordings or video sequences, for example, are all what is called a time series. Improving the deep-learning methods for such data series, advances the field of computer vision – an important component of autonomous driving. Social media, modern genome research and the diverse use of sensors today also produce enormous amounts of data, and the MCML teams are researching new methods to analyze this data.
“The potential of new machine learning methods is huge and the range of socially relevant applications limitless,” says Professor Daniel Cremers, head of the Chair for Computer Vision & Artificial Intelligence at the TUM. The MCML also aims to put its research findings into practice, which is why companies are involved.
The MCML along with other competence centers for machine learning in Berlin, in the Rhine-Ruhr area and in Tübingen will be funded for four years by the Federal Ministry of Education and Research (BMBF). These centers will receive a total of 7.5 million euros. The promoter of the BMBF initiative is the German Aerospace Center (DLR).
This news was first published on tum.de
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