Cosmology in the Machine Learning Era

The standard model of cosmology is a theoretical framework that accurately describes a large variety of cosmological observables, from the temperature anisotropies of the cosmic microwave background to the spatial distribution of galaxies. This model has a few free parameters representing fundament quantities, like the geometry and expansion rate of the Universe, the amount and nature of dark energy, and the sum of neutrino masses. Knowing the value of these parameters will improve our knowledge on the fundamental constituents and laws governing our Universe. Thus, one of most important goals of modern cosmology is to constrain the value of these parameters with the highest accuracy. In this talk I will show the large amount of cosmological information that is located on mildly to fully non-linear scales. I will then briefly introduce neural networks and show a few examples on their applications to cosmology. Finally, I will present a methodology that combines large sets of N-body and hydrodynamic simulations with machine learning techniques that can revolutionize the way we do cosmology.