A curated list of data and tools for machine learning in astronomy
- Statistics, Data Mining, and Machine Learning in Astronomy Ivezic, Connolly, Vanderplas, Gray
- Information Theory, Inference, and Learning Algorithms David Mackay
- Probabilistic Graphical Models Daphne Koller & Nir Friedman
- Gaussian Processes for Machine Learning Rasmussen and Williams
- A textbook Hogg will never write
- Data Analysis with MCMC Dan Foreman-Mackey
- An Astronomer's Introduction to Gaussian Processes Dan Foreman-Mackey
- Tools for Probabilistic Data Analysis in Python Dan Foreman-Mackey
- Statistics for Hackers Jake Vanderplas
- Supervised Machine Learning in Astronomy Josh Bloom
- Python Data Science Handbook Jake VanderPlas
- Whirlwind Tour of Python Jake VanderPlas
- Bayesian Astronomy Jake VanderPlas
- Scikit-Learn Tutorial Jake VanderPlas
- Python for Data Analysis Wes McKinney
- Astro Hack Week 2014 Seattle
- Astro Hack Week 2015 NYC
- Astro Hack Week 2016 San Francisco
- A quick tour of machine learning and statistical tools David Hogg
- Dimensionality reduction David Hogg
- Model selection and cross validation David Hogg
- Model comparison Hogg, Marshal, Brewer
- Gaussian Mixture Models Jake Vanderplas
- Celeste: Variational inference for a generative model of astronomical images Regier et al.
- Constructing a Flexible Likelihood Function for Spectroscopic Inference Czekala et al.
- Data analysis recipes: Fitting a model to data Hogg, Bovy, Lang