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Machine-learning methods have in recent years seen a great deal of use in condensed matter physics. In this thesis we apply such methods, specifically machine-learning with artificial neural networks, to the equilibrium and non-equilibrium description of the Hubbard and Hubbard-Holstein models. In the framework of ground-state density functional theory we reproduce results from the literature rega
