Machine Learning: The Art and Science of Algorithms that Make Sense of Data [Hardcover] Author: Peter Flach | Language: English | ISBN:
1107096391 | Format: PDF, EPUB
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As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
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- Hardcover: 409 pages
- Publisher: Cambridge University Press (November 12, 2012)
- Language: English
- ISBN-10: 1107096391
- ISBN-13: 978-1107096394
- Product Dimensions: 9.8 x 7.3 x 1 inches
- Shipping Weight: 2.3 pounds (View shipping rates and policies)
I'm probably to be considered a "very advanced amateur" or "informal professional" in machine learning techniques. I never studied them in school, but I presently make a living in part by coding up new ones and coming up with ML solutions for commercial problems. Confronting the field for the first time, I wondered how people learned the stuff. Most of the introductory texts just covered neural nets ... and very very badly. Neural nets are still useful, and probably the most mature of machine learning techniques, but throwing them at a beginner without context is a recipe for confusion and dismay.
This text, by contrast, barely mentions them, and puts them in their proper context for the beginner. The right way to think about machine learning is starting with *very* basic statistical techniques and probability theory, and building up from there into simple classification and scoring systems, and then on to the rest of the field. The author of this text does it the right way.
One of the difficulties of didactic texts in the subject is ... machine learning is a very diverse field. All kinds of gizmos are helpful, and there isn't an obvious taxonomy, as there is in, say, linear time series models. The author takes a very high level view; breaking the field down into geometric, probabilistic and "logical" models. I believe this to be original, and a very powerful way of looking at things for the beginner.
The progression is well thought out, and each chapter comes with a useful summary and references (one of which has already proved helpful to me) for further reading.
In my Advanced Statistics class one of the text books was The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. I found some of the chapters in this book heavy going and I had to read them several times and ask the professor lots of questions before I understood the material.
In contrast, Machine Learning by Peter Flach is a very well written, very gentle introduction to machine learning algorithms. Prof. Flach writes that he spent four years writing this book and it shows in the care with which the material is presented.
The mathematics used is algebra, exponents, summations, products and a bit of linear algebra. There are only a few places where derivatives are used (as it turns out, basic linear algebra can be used to describe many machine learning algorithms). The level of the Machine Learning makes it appropriate for an undergraduate Machine Learning course.
Machine Learning covers most of the core algorithms in machine learning. Of necessity what is provided is an overview of topics like linear regression and linear classifiers like Support Vector Machines. These are topics that are covered in depth in book like Applied Regression Analysis and An Introduction to Support Vector Machines and Other Kernel-based Learning Methods.
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