Here’s a list of free online books that I’ve come across in areas I find interesting – mathematics, statistics, data science, machine learning, etc.
Inclusion should be taken as “weak endorsement” – I haven’t read all these books but at the very least I believe what they’re doing seems interesting. For now this is just a list but I’ll add descriptions and categorize more carefully as I get a chance. (Honestly this list exists partially so I don’t have to Google for books whenever I want to find them…)
Last modified October 21, 2021.
Solon Barocas, Moritz Hardt, Arvind Narayanan. Fairness and machine learning: Limitations and Opportunities. (in progress as of March 2021).
Benjamin Baumer, Daniel Kaplan, and Nicholas Horton. Modern Data Science with R (2nd edition).
Mine Çetinkaya-Rundel and Johanna Hardin, Introduction to Modern Statistics.
Scott Cunningham, Causal Inference: The mixtape
Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for machine learning.
Jeff Erickson, Algorithms.
Richard Feynman, Robert Leighton, and Matthew Sands. The Feynman Lectures on Physics.
Garrett Grolemund and Hadley Wickham, R for data science
Moritz Hardt and Benjamin Recht, Patterns, Predictions and Actions: A story about machine learning.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Elements of Statistical Learning.
Kieran Healy, Data visualization, a practical introduction
Miguel Hernan and Jamie Robins, Causal Inference: What If.
Chip Huyen, Introduction to Machine Learning Interviews.
Rob Hyndman and George Athanasoupoulos, Forecasting: principles and practice
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning.
Alicia A. Johnson, Miles Ott, Mine Dogucu. Bayes Rules! An Introduction to Bayesian Modeling with R (in progress)
Max Kuhn and Julia Silge, Tidy Modeling with R
Max Kuhn and Kjell Johnson, Feature Engineering and Selection: A Practical Approach for Predictive Models
JD Long and Paul Teetor, R Cookbook (2nd edition)
Kevin Murphy, Probabilistic Machine Learning: An Introduction
Christoph Molnar, Interpretable Machine Learning A Guide for Making Black Box Models Explainable
Paul Roback and Julie Legler, Beyond Multiple Linear Regression: Applied General Linear Models and Multilevel Models in R.
Emily Riederer, Data disasters (in progress as of August 2021)
Darrin Speegle and Bryan Clair, Foundations of Statistics with R.
Cosma Shalizi, Advanced Data Analysis from an Elementary Point of View.
Julia Silge and David Robinson, Text mining with R: A tidy approach
Måns Thulin, Modern ststistics with R
Kush Varshney, Trustworthy Machine Learning
Kyle Walker, Analyzing US Census Data: Methods, Mapsl and Models in R.
Hadley Wickham, Mastering Shiny
Hadley Wickham, Advanced R (2nd ed). and its solutions: Malte Grosser, Henning Bumann, and Hadley Wickham, Advanced R Solutions
Pure math books (from a previous life):
Rick Durrett, Probability: Theory and examples (5th ed)
Philippe Flajolet and Robert Sedgwick, Analytic combinatorics. (This was my bible for a while during my PhD…)
Stephen Melczer, An invitation to analytic combinatorics.
Robin Pemantle (my PhD advisor!) and Mark Wilson, Analytic Combinatorics in Several Variables
Herb Wilf (RIP): Generatingfunctionology; A=B (with Zeilberger and Petkovsek); Algorithms and Complexity; Combinatorial Algorithms (with Nijenhuis); Mathematics for the Physical Sciences.
Other lists of books:
Oscar Baruffa’s Big Book of R is a collection of links to books on R.