"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."
--Devdatt Dubhashi, Professor, Department of Computer Science and
Engineering, Chalmers University, Sweden
"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."
--Daniel Barbara, George Mason University, Fairfax, Virginia, USA
"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."
--Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg,
Denmark
"I was impressed by how closely the material aligns with the needs of an
introductory course on machine learning, which is its greatest
strength...Overall, this is a pragmatic and helpful book, which is
well-aligned to the needs of an introductory course and one that I will
be looking at for my own students in coming months."
--David Clifton, University of Oxford, UK
"The first edition of this book was already an excellent introductory
text on machine learning for an advanced undergraduate or taught masters
level course, or indeed for anybody who wants to learn about an
interesting and important field of computer science. The additional
chapters of advanced material on Gaussian process, MCMC and mixture
modeling provide an ideal basis for practical projects, without
disturbing the very clear and readable exposition of the basics
contained in the first part of the book."
--Gavin Cawley, Senior Lecturer, School of Computing Sciences,
University of East Anglia, UK
"This book could be used for junior/senior undergraduate students or
first-year graduate students, as well as individuals who want to explore
the field of machine learning...The book introduces not only the
concepts but the underlying ideas on algorithm implementation from a
critical thinking perspective."
--Guangzhi Qu, Oakland University, Rochester, Michigan, USA
