  The increasingly large amount of cosmological data coming from ground-based
and space-borne telescopes requires highly efficient and fast enough data
analysis techniques to maximise the scientific exploitation. In this work, we
explore the capabilities of supervised machine learning algorithms to learn the
properties of the large-scale structure of the Universe, aiming at constraining
the matter density parameter, Omega m. We implement a new Artificial Neural
Network for a regression data analysis, and train it on a large set of galaxy
two-point correlation functions in standard cosmologies with different values
of Omega m. The training set is constructed from log-normal mock catalogues
which reproduce the clustering of the Baryon Oscillation Spectroscopic Survey
(BOSS) galaxies. The presented statistical method requires no specific
analytical model to construct the likelihood function, and runs with negligible
computational cost, after training. We test this new Artificial Neural Network
on real BOSS data, finding Omega m=0.309p/m0.008, which is remarkably
consistent with standard analysis results.
