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]