Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces

Explore the cutting-edge research on learning dynamical systems through a 52-minute seminar presented by Massimiliano Pontil from University College London at the Fields Institute. Delve into the innovative approach of Koopman Operator Regression in Reproducing Kernel Hilbert Spaces, a topic at the forefront of machine learning and dynamical systems theory. Gain insights into how this method can be applied to model complex systems and predict their behavior. Delivered as part of the Machine Learning Advances and Applications Seminar series, this talk offers a deep dive into advanced mathematical concepts and their practical applications in the field of machine learning.