"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
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