Comparison of machine learning textbooks

This page is a comparison of machine learning textbooks, especially at the so-called introductory level. It includes books that focus on presenting multiple learning methods, and excludes books that focus solely on e.g. reinforcement learning.

Comparison table

Title	Author	Length (pages)	Prerequisites	Recommendations	Code samples?
Machine Learning: A Probabilistic Perspective	Kevin P. Murphy	1008	"This book is suitable for upper-level undergraduate students and beginning graduate students in computer science, statistics, electrical engineering, econometrics, or any one else who has the appropriate mathematical background. Specifically, the reader is assumed to already be familiar with basic multivariate calculus, probability, linear algebra, and computer programming. Prior exposure to statistics is helpful but not necessary."
Introduction to Machine Learning	Alex Smola and S.V.N. Vishwanathan	196	?
Understanding Machine Learning: From Theory to Algorithms	Shai Shalev-Shwartz and Shai Ben-David	368	"We made an attempt to keep the book as self-contained as possible. However, the reader is assumed to be comfortable with basic notions of probability, linear algebra, analysis, and algorithms. The first three parts of the book are intended for first year graduate students in computer science, engineering, mathematics, or statistics. It can also be accessible to undergraduate students with the adequate background. The more advanced chapters can be used by researchers intending to gather a deeper theoretical understanding."	[1]
Pattern Recognition and Machine Learning	Christopher M. Bishop	676	"It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory."	[2]
Introduction to Machine Leaning (second edition)	Ethem Alpaydin	516	"This is an introductory textbook, intended for senior undergraduate and graduate-level courses on machine learning, as well as engineers working in the industry who are interested in the application of these methods. The prerequisites are courses on computer programming, probability, calculus, and linear algebra. The aim is to have all learning algorithms sufficiently explained so it will be a small step from the equations given in the book to a computer program. For some cases, pseudocode of algorithms are also included to make this task easier."
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (second edition)	Trevor Hastie, Robert Tibshirani, and Jerome Friedman	698	"This book is designed for researchers and students in a broad variety of fields: statistics, artificial intelligence, engineering, finance and others. We expect that the reader will have had at least one elementary course in statistics, covering basic topics including linear regression."
An Introduction to Statistical Learning with Applications in R	Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani	418	"One of the first books in this area—The Elements of Statistical Learning (ESL) (Hastie, Tibshirani, and Friedman)—was published in 2001, with a second edition in 2009. ESL has become a popular text not only in statistics but also in related fields. One of the reasons for ESL's popularity is its relatively accessible style. But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics. In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details. We have created labs illustrating how to implement each of the statistical learning methods using the popular statistical software package R. These labs provide the reader with valuable hands-on experience. This book is appropriate for advanced undergraduates or master's students in statistics or related quantitative fields or for individuals in other disciplines who wish to use statistical learning tools to analyze their data."