Aims.The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate theGaiaDR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion ofGaiaDR2.Methods.A method known as “transfer learning” is shown to be effective through extensive testing on a set of mockGaiacatalogs that are based on the FIREcosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matchedGaia/RAVE data set, which improves sensitivity to properties of the real Milky Way.Results.The result is a catalog that identifies ∼767 000 accreted stars withinGaiaDR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.