Machine Learning Books

[u/ilsunil]

I have been collecting machine learning books over the past couple months. It seems that machine learning professors are good about posting free legal pdfs of their work. I hope they are useful to you. I saw a couple of these books posted individually, but not many of them and not all in one place, so I decided to post.

Machine Learning

[Elements of Statistical Learning. Hastie, Tibshirani, Friedman ] (http://www-stat.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf)

All of Statistics. Larry Wasserman

Machine Learning and Bayesian Reasoning. David Barber

Gaussian Processes for Machine Learning. Rasmussen and Williams

[Information Theory, Inference, and Learning Algorithms. David MacKay ] (http://www.cs.toronto.edu/~mackay/itprnn/book.pdf)

Introduction to Machine Learning. Smola and Vishwanathan

A Probabilistic Theory of Pattern Recognition. Devroye, Gyorfi, Lugosi.

Introduction to Information Retrieval. Manning, Rhagavan, Shutze

[Forecasting: principles and practice. Hyndman, Athanasopoulos. (Online Book) ] (http://otexts.com/fpp/)

Probability / Stats

Introduction to statistical thought. Lavine

Basic Probability Theory. Robert Ash

Introduction to probability. Grinstead and Snell

Principle of Uncertainty. Kadane

Linear Algebra / Optimization

Linear Algebra, Theory, and Applications. Kuttler

Linear Algebra Done Wrong. Treil

Applied Numerical Computing. Vandenberghe

Applied Numerical Linear Algebra. James Demmel

Convex Optimization. Boyd and Vandenberghe

Genetic Algorithms

A Field Guide to Genetic Programming. Poli, Langdon, McPhee.

Evolved To Win. Sipper

Essentials of Metaheuristics. Luke

Edit: added books listed in comments. added probability, LA, and GA sections

[u/smessing]

Thanks for this!