  Astronomy and astrophysics are witnessing dramatic increases in data volume
as detectors, telescopes and computers become ever more powerful. During the
last decade, sky surveys across the electromagnetic spectrum have collected
hundreds of terabytes of astronomical data for hundreds of millions of sources.
Over the next decade, the data volume will enter the petabyte domain, and
provide accurate measurements for billions of sources. Astronomy and physics
students are not traditionally trained to handle such voluminous and complex
data sets. In this paper we describe astroML; an initiative, based on Python
and scikit-learn, to develop a compendium of machine learning tools designed to
address the statistical needs of the next generation of students and
astronomical surveys. We introduce astroML and present a number of example
applications that are enabled by this package.
