Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London. He also holds a visiting faculty position at the University of Johannesburg. From 2014 to 2019, Marc was a faculty member in the Department of Computing, Imperial College London. Marc’s research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making.

Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013 and received Best Paper Awards at ICRA 2014 and ICCAS 2016. In 2019, Marc co-organized the Machine Learning Summer School in London with Arthur Gretton.

In 2018, Marc has been awarded The President’s Award for Outstanding Early Career Researcher at Imperial College. He is a recipient of a Google Faculty Research Award and a Microsoft PhD Grant.

In 2018, Marc spent four months at the African Institute for Mathematical Sciences (Rwanda), where he taught a course on Foundations of Machine Learning as part of the African Masters in Machine Intelligence. He is co-author of the book Mathematics for Machine Learning, published by Cambridge University Press.

**Machine Learning:** Data-efficient machine learning, Gaussian processes, reinforcement learning, Bayesian optimization, approximate inference, deep probabilistic models

**Robotics and Control:** Robot learning, legged locomotion, planning under uncertainty, imitation learning, adaptive control, robust control, learning control, optimal control

**Signal Processing:** Nonlinear state estimation, Kalman filtering, time-series modeling, dynamical systems, system identification, stochastic information processing

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Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. …

Mathematics for Machine Learning is a book that motivates people to learn mathematical concepts. The book is not intended to cover …

Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model’s success …

Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. …