In the recent years, Machine and Deep learning techniques approaches have been introduced and tested for solving problems in astrophysics. In Gravitational Wave science many teams in the LIGO-Virgo collaboration have experimented, on simulated data or on real data of LIGO and Virgo interferometers, the power and capabilities of machine learning algorithms both for the detector noise and gravitational wave astrophysical signal characterization. The cost action CA17137 (g2net) aims to create an interdisciplinary network of Machine Learning and Gravitational Waves experts and to create collaborating teams to solve some of the problems of gravitational wave science using Machine Learning. In this seminar, I will show some of the results of the application of Machine Learning in the LIGO-Virgo collaboration and in the CA1737 cost action, dedicated to the analysis of data from gravitational wave experiments.
