Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

by Željko Ivezi?

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides

Genres: Astronomy, Reference, Mathematics, Coding, Science, Textbooks, Computer Science

552 pages, Kindle Edition

First published May 4, 2013

Reviews

[gully · Rating 5 out of 5]

Excellent book. The book comes with a companion website. Here you can find the source code for every single textbook figure. How cool is that! There is an active GitHub repo for all the textbook errata too. Highly recommended for 1) All astronomy graduate students and postdocs, 2) anyone interested in data intensive applications, 3) Professors teaching a modern astronomy methods course.
This is simply a must have book for 2015.

[Ravi · Rating 4 out of 5]

Pretty good balance of depth and breadth. Having the AstroML code be open source is great. I wish there was more on neural networks (e.g., CNNs, GANs), which are mentioned in the book as if they were explained, but actually are not touched upon. Also there is a lot of mention of cross validation before the term is ever defined. Overall a very solid stats and ML reference.

[Benji · Rating 4 out of 5]

Practical indeed, contains intuitive explanations of (astro)statistical analysis and data mining techniques while presenting a wealth of tips about trade-offs in practice.