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awesome astrodata\nA curated list of data and tools for machine learning in astronomy\n\n\n# Books\n- [Statistics, Data Mining, and Machine Learning in Astronomy](http://press.princeton.edu/titles/10159.html) Ivezic, Connolly, Vanderplas, Gray\n- [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/) David Mackay\n- [Probabilistic Graphical Models](https://mitpress.mit.edu/books/probabilistic-graphical-models) Daphne Koller \u0026 Nir Friedman\n- [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/) Rasmussen and Williams\n- [A textbook Hogg will never write](https://github.com/davidwhogg/DataAnalysisRecipes)\n\n\n# Talk slides\n- [Data Analysis with MCMC](https://speakerdeck.com/dfm/data-analysis-with-mcmc) Dan Foreman-Mackey\n- [An Astronomer's Introduction to Gaussian Processes](https://speakerdeck.com/dfm/an-astronomers-introduction-to-gaussian-processes-v2) Dan Foreman-Mackey\n- [Tools for Probabilistic Data Analysis in Python](https://speakerdeck.com/dfm/pyastro16) Dan Foreman-Mackey\n- [Statistics for Hackers](https://speakerdeck.com/jakevdp/statistics-for-hackers) Jake Vanderplas\n\n\n# Jupyter Notebooks\n- [Supervised Machine Learning in Astronomy](https://github.com/AstroHackWeek/AstroHackWeek2014/tree/master/day4) Josh Bloom\n- [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook) Jake VanderPlas\n- [Whirlwind Tour of Python](https://github.com/jakevdp/WhirlwindTourOfPython) Jake VanderPlas\n- [Bayesian Astronomy](https://github.com/jakevdp/BayesianAstronomy) Jake VanderPlas\n- [Scikit-Learn Tutorial](https://github.com/jakevdp/sklearn_tutorial) Jake VanderPlas\n- [Python for Data Analysis](https://github.com/wesm/pydata-book) Wes McKinney\n- [Astro Hack Week 2014](https://github.com/AstroHackWeek/AstroHackWeek2014) Seattle\n- [Astro Hack Week 2015](https://github.com/AstroHackWeek/AstroHackWeek2015) NYC\n- [Astro Hack Week 2016](https://github.com/AstroHackWeek/AstroHackWeek2016) San Francisco\n\n\n# Videos\n\n- [A quick tour of machine learning and statistical tools](https://www.youtube.com/watch?v=aA3qdegi8Vw) David Hogg\n- [Dimensionality reduction](https://www.youtube.com/watch?v=CvBCmWc8iBE) David Hogg\n- [Model selection and cross validation](https://www.youtube.com/watch?v=uaztY3Lbr4A) David Hogg\n- [Model comparison](https://www.youtube.com/watch?v=sm-yFQcaD4Q) Hogg, Marshal, Brewer\n- [Gaussian Mixture Models](https://www.youtube.com/watch?v=W0XECm4-3LI) Jake Vanderplas\n\n\n\n# Examples of Probabilistic Graphical Models in Astronomy\n- [Celeste: Variational inference for a generative model of\nastronomical images](http://www.stat.berkeley.edu/~jeff/publications/regier2015celeste.pdf) Regier et al.\n- [Constructing a Flexible Likelihood Function for Spectroscopic Inference](http://adsabs.harvard.edu/abs/2015ApJ...812..128C) Czekala et al.\n\n\n# Lecture notes\n- [Data analysis recipes: Fitting a model to data](https://arxiv.org/abs/1008.4686) Hogg, Bovy, Lang\n\n\n# Workshops\n- [AstroHackWeek](http://astrohackweek.org/)\n- [.Astronomy](http://dotastronomy.com/)\n- [AAS Hack Day](http://www.astrobetter.com/wiki/AASHackDay)\n\n# Institutes\n- [NYU Center for Data Science](http://cds.nyu.edu/)\n- [UC Berkeley Institute for Data Science](https://bids.berkeley.edu/)\n- [UW eScience Institute](http://escience.washington.edu/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/gully%2Fawesome-astrodata/projects"}