Best 4 free math books for deepening your machine learning skills

The best things in life aren’t things – but free books. At least if you want to spend the next few weeks to take your machine learning to another level.
We’ve selected five great books that help you to understand one important aspect in machine learning in a very profound way. Thanks to the Open Access initiative, all of these works are available for free:

Elements of Statistical Learning

The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman

If you need a refresher on your machine learning methods, ESL is the book to go to. From Lasso to boosted trees and ensemble learning – this beautiful typeset work covers all the bases for your professional life as a data scientist.

Here’s the latest 12th printing from January 2017 as PDF download.

Convex Optimization by Stephen Boyd and Lieven Vandenberghe

Convex Optimization

If you have spend some years in machine learning, the probability is very high, that you’ve stumbled upon convex optimization problems. The theory and methods around convex optimization has been around a long time. But until a few decades, they were thought to be mostly of theoretical value. Today, convex optimization is e.g. an important part of Deep Learning and many smart things around are powered by these algorithms.

Here you can download the full book by the Stanford professor for free – and there’s a lot of additional material on the website and even an online course.

Group Representations in Probability and Statistics by Persi Diaconis

Group Representations in Probability and Statistics

This book goes back to Diaconis’ lecture notes for his course on this topic at Harvard in the 1980s. There are a lot of situations where data scientists have to deal with rankings, e.g. consumers having to rank products in a survey. These mathematical problems can be solved by applying group theory.

But wait, there’s more: As Diaconis is also a magician, the shuffling of cards also plays quite a role in this work.

The book is available at project euclid.

Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman and Jeff Ullman

Mining of Massive Datasets

And finally, for something more accessible: This book is already a classic on big data techniques for processing large data sets. If you haven’t already, you really take a look at the 2nd edition of the book that also includes mining large graphs and map-reduce programming. You can also take a look at the first chapters of the upcoming 3rd edition.

Here’s the book’s homepage with a lot of additional information.

I hope, there are some useful additions to your reading list. Looking forward to your feedback about the books. What was especially useful? Which books are missing from this list?