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"alias": "video/2035/opening-up-astronomy-with-python-and-astroml-sci",
"category": "SciPy 2013",
"copyright_text": "",
"description": "",
"duration": null,
"id": 2035,
"language": "eng",
"quality_notes": "",
"recorded": "2013-07-02",
"related_urls": [
"slug": "opening-up-astronomy-with-python-and-astroml-sci",
"speakers": [],
"summary": "Authors: Vanderplas, Jake, University of Washington; Ivezic, Zeljko,\nUniversity of Washington; Connolly, Andrew, University of Washington\n\nTrack: General\n\nAs astronomical data sets grow in size and complexity, automated machine\nlearning and data mining methods are becoming an increasingly\nfundamental component of research in the field. The astroML project\n(, first released in fall 2012, provides a\ncommon repository for practical examples of the data mining and machine\nlearning tools used and developed by astronomical researchers, written\nin python. The astroML module offers a host of general data analysis and\nmachine learning routines, loaders for openly-available astronomical\ndatasets, and fast implementations of specific computational methods\noften used in astronomy and astrophysics. The associated website\nfeatures hundreds of examples of these routines in action, using real\ndatasets. In this talk I'll go over some of the highlights of the\nastroML code and examples, and discuss how we've used astroML as an aid\nfor student research, hands-on graduate astronomy curriculum, and the\nsharing of research tools and results.\n",
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"title": "Opening Up Astronomy with Python and AstroML; SciPy 2013 Presentation",
"videos": [
"length": 0,
"type": "youtube",
"url": ""
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