Average Reviews:
(More customer reviews)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). Of these, I think the first Bishop
book is the only other text suitable for a beginner, but it doesn't have the
explanation-by-programming approach and is also now a bit dated (Marsland
includes modern topics such as manifold learning, ensemble learning, and a bit
of graphical models). Friedman et al. is a good collection of algorithms,
including ones that are not presented in Marsland; it is a bit dry however.
The new Bishop is probably the deepest and best current text, but it is
probably most suited for PhD students. Duda et al would be a good book at a
Masters level though its coverage of modern techniques is more limited. Of
course these are just my impressions. Machine learning is a broad subject and
anyone using these algorithms will eventually want to refer to several of these books.
For example, the first Bishop covers the normalized flavor of radial basis
functions (a favorite technique for me), and each of the mentioned books has
their own strengths.
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Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
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