Astro Machine Learning

The astroML project was started in 2012 to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy, by Željko Ivezić, Andrew Connolly, Jacob Vanderplas, and Alex Gray. 

The astroML Python package is publicly available and designed as a repository of statistical routines  and machine learning tools for astrophysics. It builds on the scientific Python ecosystem, on well known libraries such as Numpy, Scipy, Scikit-learn, and Astropy; extending the functionality available in these general-purpose libraries.  

astroML is designed to be a resource for both researchers and students of astronomy and Python.   It is envisioned to be a community resource, with the development and submission of new algorithms, data sets, and examples provided by GitHub’s collaborative coding interface. In addition to being used for astronomical research, several university courses build on astroML, for example at the University of Washington, University of Cambridge, and Drexel University to list a few.

astroML strives to bring the astronomical community closer to the ideals of Reproducible Research, in which research papers are accompanied by well-written code to reproduce, check, and extend the results. With this in mind we share the source code used  to generate the figures in both editions of the textbook in a separate GitHub repository.

Updates and news about astroML project can be found here.