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	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	?