One of the biggest challenges in condensed matter physics is to calculate properties of materials taking into account the quantum many-body nature of matter. While the Coulomb interaction is universal, its effects cannot be separated from the specific material under analysis, which leads to a huge theoretical and computational effort. Recently, Machine Learning (ML) has raised new hopes as a tool that could be used to screen and predict properties of broad classes of materials. Indeed, the statistical structure of ML tools is particularly well suited to deal with quantum mechanics, thanks to the statistical information encoded in the state vector, and in recent years ML has been used for addressing various classes of problems.