# AstroML Interactive Book#

astroML is a Python module for machine learning and data mining that accompanies the book “Statistics, Data Mining, and Machine Learning in Astronomy”, by Željko Ivezić, Andrew Connolly, Jacob Vanderplas, and Alex Gray. astroML is built on numpy, scipy, scikit-learn, matplotlib, and astropy, and contains a growing library of statistical and machine learning routines for analyzing astronomical data.

In this interactive book we provide notebooks that describe the statistical and machine learning methods used in astroML together with code that runs these methods on existing astronomical data sets. The structure of this interactive book follows the chapters of “Statistics, Data Mining, and Machine Learning in Astronomy”. Each notebook can viewed through the browser (with navigation links at the side of the page), be downloaded to your own computer, or be executed directly using Binder or Google Colab

## Content#

- Chapter 1: Introduction and Data Sets
- Chapter 2: Fast Computation and Massive Datasets
- Chapter 3: Probability and Statistical Distributions
- Chapter 4: Classical Statistical Inference
- Chapter 5: Bayesian Statistical Inference
- Chapter 6: Searching for Structure in Point Data
- Chapter 7: Dimensionality and its Reduction
- Chapter 8: Regression and Model Fitting
- Chapter 9: Classification
- Chapter 10: Time Series Analysis

## Contributing#

For contributing guidelines, please follow the documentation in the astroML-notebooks github repository.