Introduction. Linear Discriminants. The Multi-Layer Perceptron. Radial Basis Functions and Splines. Support Vector Machines. Learning with Trees. Decision by Committee: Ensemble Learning. Probability and Learning. Unsupervised Learning. Dimensionality Reduction. Optimization and Search. Evolutionary Learning. Reinforcement Learning. Markov Chain Monte Carlo (MCMC) Methods. Graphical Models. Python.
Stephen Marsland is a professor of scientific computing and the postgraduate director of the School of Engineering and Advanced Technology (SEAT) at Massey University. His research interests in mathematical computing include shape spaces, Euler equations, machine learning, and algorithms. He received a PhD from Manchester University
"I thought the first edition was hands down, one of the best texts
covering applied machine learning from a Python perspective. I
still consider this to be the case. The text, already extremely
broad in scope, has been expanded to cover some very relevant
modern topics … I highly recommend this text to anyone who wants to
learn machine learning … I particularly recommend it to those
students who have followed along from more of a statistical
learning perspective (Ng, Hastie, Tibshirani) and are looking to
broaden their knowledge of applications. The updated text is very
timely, covering topics that are very popular right now and have
little coverage in existing texts in this area."
—Intelligent Trading Tech blog, April 2015"The book's emphasis on
algorithms distinguishes it from other books on machine learning
(ML). This is further highlighted by the extensive use of Python
code to implement the algorithms. ... The topics chosen do reflect
the current research areas in ML, and the book can be recommended
to those wishing to gain an understanding of the current state of
the field."
—J. P. E. Hodgson, Computing Reviews, March 27, 2015"I have been
using this textbook for an undergraduate machine learning class for
several years. Some of the best features of this book are the
inclusion of Python code in the text (not just on a website),
explanation of what the code does, and, in some cases, partial
numerical run-throughs of the code. This helps students understand
the algorithms better than high-level descriptions and equations
alone and eliminates many sources of ambiguity and
misunderstanding."
—Daniel Kifer"This book will equip and engage students with its
well-organised and -presented material. In each chapter, they will
find thorough explanations, figures illustrating the discussed
concepts and techniques, lots of programming (Python) and worked
examples, practice questions, further readings, and a support
website. The book will also be useful to professionals who can
quickly inform and refresh their memory and knowledge of how
machine learning works and what are the fundamental approaches and
methods used in this area. As a whole, it provides an essential
source for machine learning methodologies and techniques, how they
work, and what are their application areas."
—Ivan Jordanov, University of Portsmouth, UKPraise for the First
Edition:"… liberally illustrated with many programming examples,
using Python. It includes a basic primer on Python and has an
accompanying website. It has excellent breadth and is comprehensive
in terms of the topics it covers, both in terms of methods and in
terms of concepts and theory. … I think the author has succeeded in
his aim: the book provides an accessible introduction to machine
learning. It would be excellent as a first exposure to the subject,
and would put the various ideas in context …"
—David J. Hand, International Statistical Review (2010), 78"If you
are interested in learning enough AI to understand the sort of new
techniques being introduced into Web 2 applications, then this is a
good place to start. … it covers the subject matter of many an
introductory course on AI and it has references to the source
material and further reading but it is written in a fairly casual
style. Overall it works and much of the mathematics is explained in
ways that make it fairly clear what is going on … . This is a
suitable introduction to AI if you are studying the subject on your
own and it would make a good course text for an introduction and
overview of AI."
—I-Programmer, November 2009
Ask a Question About this Product More... |