Rogers S., Girolami M. First Course in Machine Learning

Second Edition. — CRC Press, 2017. — 428 p. — ISBN: 978-981-10-4288-1.A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts.FeaturesIntroduces the main algorithms and ideas that underpin machine learning techniques and applications
Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations
Covers modern machine learning research and techniques
Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models
Offers Python, R, and MatLAB code on accompanying website.