Abstract

We are witnessing an unprecedented growth in the volume and complexity of astronomical data: current surveys, telescopes and instruments are providing massive amount of raw data, images and spectra. This situation will become even more extreme in the future, in particular with the upcoming telescopes LSST, SKA, and Euclid. In the era of "Big Data", a new generation of astronomers will need to deal routinely with tera- and even petabytes of information. The analysis of these data cannot be carried out by humans in the traditional way, and key decisions in the process will have to rely on numerical algorithms. In that sense, accessing and digesting data and extracting the relevant information from them will pose significant challenges to astronomers, both at the technical and at the analytical level. Machine learning techniques, which belong to the realms of computer science, applied mathematics and statistics, provide key tools to be used for the task. Acknowledging the importance for the IAC to take part in this revolution from an early stage, the XXX Canary Islands Winter School will allow the students to become acquainted with the main current developments in this field, and with the new techniques to be used. The Winter School will consist of interdisciplinary lectures and practical work focussed on dealing with big astronomical data sets. A particular focus is on statistical tools and machine learning techniques.