The Ising model is a graphical model whose parameters can be tuned in order to describe stationary distributions of binary variables. In many practical problems in different domains – e.g. physics, biology, neuroscience, finance, sociology – the topology of the graph and the values of the couplings are unknown and they need to be reconstructed from the data. The inverse Ising problem aims to find the parameters of the model that best fit the data. We propose a new algorithm to learn the network of the interactions of a pairwise Ising model, based on the pseudo-likelihood method. Our present implementation is particularly suitable to address the case of sparse underlying topologies and it is based on a careful search of the most important parameters in their high dimensional space.