https://github.com/gully/awesome-astrodata

Awesome list for astronomy data and resources for self-learning
https://github.com/gully/awesome-astrodata

List: awesome-astrodata

Last synced: 16 days ago
JSON representation

Awesome list for astronomy data and resources for self-learning

• Host: GitHub
• URL: https://github.com/gully/awesome-astrodata
• Owner: gully
• License: mit
• Created: 2016-07-08T06:42:24.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-08-19T05:35:31.000Z (over 8 years ago)
• Last Synced: 2024-04-22T04:38:25.032Z (8 months ago)
• Homepage:
• Size: 2.93 KB
• Stars: 25
• Watchers: 2
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• ultimate-awesome - awesome-astrodata - Awesome list for astronomy data and resources for self-learning. (Other Lists / Monkey C Lists)

README

        
# awesome astrodata

A curated list of data and tools for machine learning in astronomy

# Books

- [Statistics, Data Mining, and Machine Learning in Astronomy](http://press.princeton.edu/titles/10159.html) Ivezic, Connolly, Vanderplas, Gray

- [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/) David Mackay

- [Probabilistic Graphical Models](https://mitpress.mit.edu/books/probabilistic-graphical-models) Daphne Koller & Nir Friedman

- [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/) Rasmussen and Williams

- [A textbook Hogg will never write](https://github.com/davidwhogg/DataAnalysisRecipes)

# Talk slides

- [Data Analysis with MCMC](https://speakerdeck.com/dfm/data-analysis-with-mcmc) Dan Foreman-Mackey

- [An Astronomer's Introduction to Gaussian Processes](https://speakerdeck.com/dfm/an-astronomers-introduction-to-gaussian-processes-v2) Dan Foreman-Mackey

- [Tools for Probabilistic Data Analysis in Python](https://speakerdeck.com/dfm/pyastro16) Dan Foreman-Mackey

- [Statistics for Hackers](https://speakerdeck.com/jakevdp/statistics-for-hackers) Jake Vanderplas

# Jupyter Notebooks

- [Supervised Machine Learning in Astronomy](https://github.com/AstroHackWeek/AstroHackWeek2014/tree/master/day4) Josh Bloom

- [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook) Jake VanderPlas

- [Whirlwind Tour of Python](https://github.com/jakevdp/WhirlwindTourOfPython) Jake VanderPlas

- [Bayesian Astronomy](https://github.com/jakevdp/BayesianAstronomy) Jake VanderPlas

- [Scikit-Learn Tutorial](https://github.com/jakevdp/sklearn_tutorial) Jake VanderPlas

- [Python for Data Analysis](https://github.com/wesm/pydata-book) Wes McKinney

- [Astro Hack Week 2014](https://github.com/AstroHackWeek/AstroHackWeek2014) Seattle

- [Astro Hack Week 2015](https://github.com/AstroHackWeek/AstroHackWeek2015) NYC

- [Astro Hack Week 2016](https://github.com/AstroHackWeek/AstroHackWeek2016) San Francisco

# Videos

- [A quick tour of machine learning and statistical tools](https://www.youtube.com/watch?v=aA3qdegi8Vw) David Hogg

- [Dimensionality reduction](https://www.youtube.com/watch?v=CvBCmWc8iBE) David Hogg

- [Model selection and cross validation](https://www.youtube.com/watch?v=uaztY3Lbr4A) David Hogg

- [Model comparison](https://www.youtube.com/watch?v=sm-yFQcaD4Q) Hogg, Marshal, Brewer

- [Gaussian Mixture Models](https://www.youtube.com/watch?v=W0XECm4-3LI) Jake Vanderplas

# Examples of Probabilistic Graphical Models in Astronomy

- [Celeste: Variational inference for a generative model of

astronomical images](http://www.stat.berkeley.edu/~jeff/publications/regier2015celeste.pdf) Regier et al.

- [Constructing a Flexible Likelihood Function for Spectroscopic Inference](http://adsabs.harvard.edu/abs/2015ApJ...812..128C) Czekala et al.

# Lecture notes

- [Data analysis recipes: Fitting a model to data](https://arxiv.org/abs/1008.4686) Hogg, Bovy, Lang

# Workshops

- [AstroHackWeek](http://astrohackweek.org/)

- [.Astronomy](http://dotastronomy.com/)

- [AAS Hack Day](http://www.astrobetter.com/wiki/AASHackDay)

# Institutes

- [NYU Center for Data Science](http://cds.nyu.edu/)

- [UC Berkeley Institute for Data Science](https://bids.berkeley.edu/)

- [UW eScience Institute](http://escience.washington.edu/)