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Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Pattern Recognition) 1st Edition

4.0 out of 5 stars 29 customer reviews
ISBN-13: 978-1420067187
ISBN-10: 1420067184
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Editorial Reviews

Review

… 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 …
This book also includes the first occurrence I have seen in print of a reference to a zettabyte of data (1021 bytes) ― a reference to "all the world’s computers" being estimated to contain almost a zettabyte by 2010.
―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

About the Author

Massey University, Palmerston North, New Zealand
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Product Details

  • Paperback: 406 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (April 1, 2009)
  • Language: English
  • ISBN-10: 1420067184
  • ISBN-13: 978-1420067187
  • Product Dimensions: 9.3 x 6.3 x 0.9 inches
  • Shipping Weight: 1.6 pounds
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (29 customer reviews)
  • Amazon Best Sellers Rank: #794,190 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Paperback Verified Purchase
This is an good book on machine learning for students at the advanced
undergraduate or Masters level, or for self study, particularly if
some of the background math (eigenvectors, probability theory, etc)
is not already second nature.

Although I am now familiar with much of the math in this area and consider
myself to have intermediate knowledge of machine learning, I can still recall
my first attempts to learn some mathematical topics. At that time my approach
was to implement the ideas as computer programs and plot the results. This
book takes exactly that approach, with each topic being presented both
mathematically and in Python code using the new Numpy and Scipy libraries.
Numpy resembles Matlab and is sufficiently high level that the book code
examples read like pseudocode.

(Another thing I recall when I was first learning was the mistaken
belief that books are free from mistakes. I've since learned to
expect that every first edition is going to have some, and doubly so
for books with math and code examples. However the fact that many of the examples
in this book produce plots is reassuring.)

As mentioned I have only intermediate knowledge of machine learning, and
have no experience with some techniques. I learned regression trees
and ensemble learning from this book -- and then implemented an ensemble
tree classifier that has been quite successful at our company.

Some other strong books are the two Bishop books (Neural Networks for Pattern
Recognition; Pattern Recognition and Machine Learning),
Friedman/Hastie/Tibshirani (Elements of Statistical Learning) and
Duda/Hart/Stork (Pattern Classification).
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Format: Paperback
Modern Machine Learning is deeply statistical and mathematical in nature, and as others have said, this book aims to trade off some rigor in favor of a more intuitive approach. That in itself is not a bad idea; there should be room for a book that gives the reader a working knowledge of the more important techniques, even if they don't necessarily understand how they work at a deep level. Unfortunately, this book stumbles quite badly in many respects.

We chose the book for an introductory course in Machine Learning at my university, as our students often don't have the level of mathematical background necessary for treatments like the Bishop book. However, I and my colleague often had to resort to essentially replacing entire chapters of material due to serious flaws in the text. In some cases, the author gives definitions for commonly available terms that are simply factually incorrect. For example, the chapter on Evolutionary Algorithms repeatedly confuses parent selection and elitism, states that crossover cannot be defined for non-binary representations, and other similar mistakes. In the chapter on Reinforcement Learning, most of the material is at least correct, but does not progress in any meaningful manner. For instance, the author introduces TD(') methods using execution traces, gives the formula for updating the trace, and then simply stops. There is no indication of what you should do with this value once it's been calculated. There are quite a number of these sort of issues in the book.

I would also concur with the other reviewers who felt that the idea of stripping away unnecessary mathematical formalism has simply been taken too far here.
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Format: Paperback
There are two or three things that I really like about this book.

First, so many books of this type seem to leave off the first 20 or so pages that should tell you what it is that they are trying to do. Instead of assuming that you know what Machine Learning is all about, this book has an initial chapter that explains in simple terms what we are trying to do here.

Second, instead us using some kind of psuedocode, the examples are written in a standard language, Python. Python is a free language in the open source community so students can get/use it without incurring the costs associated with some other languages. It is also intended to be very readable which makes the demonstration programs easier to understand. There is also a chapter on programming in Python.

Machine learning usually is put into the computer science department in universities, and as a result is usually taught to computer science students. In fact, machine learning also requires more mathematical background and more engineering background than most computer science students have. The approach used in this book is to discuss algorithms used in machine learning, but to do so by stressing how and why they work.

The author says that the book is suitable for undergraduate use. Yes, it is, but for the rather advanced undergraduate or even early graduate level student
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Format: Paperback Verified Purchase
I first saw this book on a colleague's bookshelf; i picked it up and briefly looked through it. The simple diagrams and the relative lack of equations (compared to e.g., Bishop) might suggest to you that it's a 'beginner' text--and by that i mean that the textbook is only an introduction to ML and doesn't teach you enough so that you can begin writing ML code to solve real classification/regression problems. That's what i though at first, and i was wrong. This is an introductory text, but only in the sense that it's accessible to more or less anyone, but this book's explanation/theory and the practical examples (in python) are brilliantly integrated--the explanation (often summarizing two or three pages of terse equations found in other textbooks, in a single paragraph) helped me grok the code, and the code reinforced the theory behind the algorithm.

I don't think there's another ML book like this--it's aimed right at the blind spot framed by applied math reference-type books such as Bishop on one end, and books like 'Programming Collective Intelligence' which are dense with working ML code, but light on theory.

I also like this book because the code is written in NumPy, rather than in the Python standard library code. NumPy is what you would use 'in the real world' to code an ML algorithm, and if you understand the matrix-driven syntax, then the code is far more concise (e.g., no triply nested recursive loops) than the same algorithms coded using just the Python standard library.

In sum, an excellent book.
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