Where can I learn quantum chemistry
Machine Learning - Techniques, Algorithms and Perspectives
Klaus-Robert Müller, TU Berlin
About the lecture:
With the advent of new, faster and more precise measurement techniques, the amount and quality of data has increased so much that experimental researchers and industry are reaching their limits. In order to generate meaningful information from this unmanageable mass of data, one has to ask the “right” questions of the data. Successful big data technology must therefore go beyond simply collecting data and ask interesting questions.
Machine learning is based on the mathematical formalization of such questions. Machine learning for the so-called brain computer interface is a particular challenge. Here, brain signals are decoded in order to use them to control objects, for example for spelling, for manipulating a cursor or in computer games. Applications of this type are of great importance for patients with locked-in syndrome. People with this disease are completely paralyzed but fully conscious. In addition to applications in the neurosciences, the latest results of machine learning in quantum chemistry will also be presented.
Prof. Dr. Klaus-Robert Müller has been a computer science professor at the TU Berlin since 2006 and was spokesman for the Berlin Bernstein Focus Neurotechnology until 2014, now he is co-director of the Berlin Big Data Center.
He studied physics in Karlsruhe from 1984 to 1989, where he also received his doctorate in computer science in 1992. This was followed by a PostDoc at GMD FIRST in Berlin from 1992-1994 and an EU STP Fellowship at the University of Tokyo from 1994-1995. From 1995 he built up the intelligent data analysis group at GMD FIRST (later Fraunhofer FIRST) and headed it until 2008. 1999-2006 he was a professor of computer science at the University of Potsdam. Klaus-Robert Müller received the Olympus Prize for Pattern Recognition in 1999 and the SEL-Alcatel Prize for Technical Communication in 2006 and the Governing Mayor's Berlin Science Prize in 2014. In 2012 he was elected to the German National Academy of Sciences - Leopoldina. His research interests are neural networks, intelligent data analysis, machine learning, statistical signal processing and statistical learning theory with a focus on applications in physics, cheminformatics, genome analysis and neurosciences. Since 2000, one of his special scientific priorities has been researching the interface between the brain and machine: the non-invasive EEG-based brain computer interfacing.
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