Machine Learning

The Art and Science of Algorithms that Make Sense of Data

by Peter Flach, Intelligent Systems Laboratory, University of Bristol, United Kingdom
Published in September 2012 by Cambridge University Press.
[ CUP (offering 20% discount on list price) | Google Books ]

About the book

This book is an introductory text on machine learning. The style of the book is such that it can be used as a textbook for an advanced undergraduate or graduate course, at the same time aiming at interested academics and professionals with a background in neighbouring disciplines. The material includes necessary mathematical detail, but emphasises intuitions and how-to.

The challenge in writing an introductory machine learning text is to do justice to the incredible richness of the machine learning field without losing sight of the unifying principles. One way in which this is achieved in this book is by separate and extensive treatment of tasks and features, both of which are common across any machine learning approaches. Covering a wide range of logical, geometric and statistical models, the book will be one of the most comprehensive machine learning texts around. It is also up-to-date, covering state-of-the-art topics such as matrix methods and learning in networks.

Table of contents

Linked chapter titles below are currently being made available for download as a draft (requires signing up with your email address).
Prologue: A machine learning sampler
1 The ingredients of machine learning
2 Binary classification and related tasks
3 Beyond binary classification
4 Concept learning
5 Tree models
6 Rule models
7 Linear models
8 Distance-based models
9 Statistical models
10 Features
11 In brief: Model ensembles
12 In brief: Machine learning experiments
Epilogue: Where to go from here
Important points to remember
Bibliography
Index
For additional materials such as lecture slides click here.
algorithm association average beak binary boundary called case class classification classifier clustering consider coverage covered curve data decision descriptive different distance e-mail estimates example feature function general gills given gives grouping ham instance item labelled leaf learning length linear list loss lottery machine matrix means models negative number obtain order parameters performance points positive possible predictive probabilistic probability problem ranking regression result roc rule scores segment sets space spam tasks teeth terms test threshold training tree true used values variables vector viagra words yes
created at TagCrowd.com

Your feedback is appreciated!

Please use this link to submit feedback.

Last change: 24 August, 2012