I discuss a novel machine learning model for gravitational waves from precessing binary black holes.
Deep learning algorithms have the potential to dramatically improve predictive models of chirp like gravitational waves.
I discuss how systematic errors in waveform models can affect parameter inference and how these errors can be incorporated into the waveform model construction.
I give a comparison of classical and machine learning methods for regression of gravitational wave data.