Petros Koumoutsakos ETH Zurich The field of fluid mechanics experiences today a shift from first principles to data driven approaches. While fluid mechanics has always involved massive volumes of data from experiments, field measurements, and large-scale simulations and despite early connections dating back to Kolmogorov, the link between Fluid Mechanics and Machine Learning (ML) has been weak. The situation is rapidly changing with ML algorithms entering in numerous efforts for modeling, optimizing, and controlling fluid flows. In this talk I will present works from our group on the interface of Fluid Mechanics and ML ranging from low order models for turbulent flows to deep reinforcement learning algorithms and bayesian experimental design for collective swimming. I hope to demonstrate that ML has the potential to augment, and possibly even transform, current lines of fluid mechanics research. I will also discuss how fluid mechanics problems and approaches may be of value to the ML community.