Astrophysics has always been at the forefront of data analysis. It has led to advancements in image processing and numerical simulations. The coming decade is bringing qualitatively new and larger datasets than ever before. The next generation of observational facilities will produce an explosion in the quantity and quality of data for the most distant sources, such as the first galaxies and first quasars. Quasars are the most energetic objects in the universe, reaching luminosity up to 10^14 that of the Sun. Their emission is powered by giant black holes that convert matter into energy according to the famous Einstein’s equation E = mc^2. The largest progress will occur in quasar spectroscopy. Detailed measurements of spectrum of quasar light, as it is being emitted near the central black hole and partially absorbed by clouds of gas on the way to the observer on Earth, allows for a particularly powerful probe of quasar environment. Because spectra of different chemical elements are unique, spectroscopy allows to study not only the overall properties of matter such as density and temperature, but also the detailed chemical composition of the intervening matter. However, the interpretation of these spectra is made very challenging by the many sources contributing to the absorption of light. In order to take a full advantage of this new window into the nature of supermassive black holes we need detailed theoretical understanding of the origin of quasar spectral features. In a MIDAS PODS project we are applying machine learning to model and extract such features. We are training the models using data from the state-of-the-art numerical simulations of the early universe. This approach is fundamentally different from traditional astronomical data analysis. We have only started learning what information can be extracted and still looking for a new framework to interpret these data.