In this paper we consider Probabilistic logic programming (PLP) models that
are amenable to lifted inference, called liftable PLP, and present an algorithm for performing parameter and structure learning of these models from data. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER.