Machine Learning, Statistics, and Data Mining for Heliophysics¶
By Monica Bobra and James Mason
Contributions by Matthew R. Argall, Carlos José Díaz Baso, Téo Bloch, Yue Chen, Mark Cheung, Tobías Felipe, David Fouhey, Richard Galvez, Meng Jin, Andrés Muñoz-Jaramillo, Brandon Panos, Rafael Pires de Lima, Allan R. Macneil, Leland McInnes, Mathew Owens, Marek Petrik, Andrés Asensio Ramos, Colin Small, Alexandre Szenicer, Rajat Thomas, Clare Watt, and Paul Wright.
This is a book about machine learning, statistics, and data mining for heliophysics.
This book includes a collection of interactive Jupyter notebooks, written in Python, that explicitly shows the reader how to use machine learning, statistics, and data minining techniques on various kinds of heliophysics data sets to reproduce published results.
The contents of this book are licensed for free consumption under the following license:
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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2018-09-10 |
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2019-02-22 |
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2020-05-11 |
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2021-02-08 |
If you’d like to cite the evolving book, instead of a specific version, use the following DOI: https://doi.org/10.5281/zenodo.1412824.