Patrick van der Smagt


Patrick van der Smagt is director of the open-source Volkswagen Group Machine Learning Research Lab in Munich, focussing on probabilistic deep learning for time series modelling, optimal control, and quantum machine learning. He previously directed a lab as professor for machine learning and biomimetic robotics at the Technical University of Munich while leading the machine learning group at the research institute fortiss, and was the head of bionics and assistive robotics at the DLR Oberpfaffenhofen. Long before that, he did his PhD and MSc at Amsterdam’s universities. Besides publishing numerous papers and patents on machine learning, robotics, and motor control, he has won various awards, including the 2013 Helmholtz-Association Erwin Schrödinger Award, the 2014 King-Sun Fu Memorial Award, the 2013 Harvard Medical School/MGH Martin Research Prize, and various best-paper awards. He is founding chairman of a non-for-profit organisation for Assistive Robotics for tetraplegics and co-founder of various companies.

Efficient AI: Bayesian machine learning and optimal control

Level: Advanced

Neural networks have proven themselves as excellent inference methods for learning nonlinear relationships in large data sets. But this does not suffice for systems with few or unsupervised data, such as feedback control loops. Here the merit of generative latent-variable models becomes clear. In my talk, I will explain how a Bayesian approach to neural networks can bring us towards more generally applicable artificially intelligent systems. And how adding control can create such.

This in-depth talk is intended for machine-learning experts.