xvi
PREFACE
A third principle that guided the writing of this book was that it should
present a balance of theory and practice. Machine learning theory attempts to an-
swer questions such as "How does learning performance vary with the number of
training examples presented?" and "Which learning algorithms are most appropri-
ate for various types of learning tasks?" This book includes discussions of these
and other theoretical issues, drawing on theoretical constructs from statistics, com-
putational complexity, and Bayesian analysis. The practice of machine learning
is covered by presenting the major algorithms in the field, along with illustrative
traces of their operation. Online data sets and implementations of several algo-
rithms are available via the World Wide Web at
http://www.cs.cmu.edu/-tom1
mlbook.html. These include neural network code and data for face recognition,
decision tree learning, code and data for financial loan analysis, and Bayes clas-
sifier code and data for analyzing text documents.
I
am grateful to a number of
colleagues who have helped to create these online resources, including Jason Ren-
nie, Paul Hsiung, Jeff Shufelt, Matt Glickman, Scott Davies, Joseph O'Sullivan,
Ken Lang, Andrew McCallum, and Thorsten Joachims.
ACKNOWLEDGMENTS
In writing this book,
I
have been fortunate to be assisted by technical experts
in many of the subdisciplines that make up the field of machine learning. This
book could not have been written without their help.
I
am
deeply indebted to
the following scientists who took the time to review chapter drafts and, in many
cases, to tutor me and help organize chapters in their individual areas of expertise.
Avrim Blum, Jaime Carbonell, William Cohen, Greg Cooper, Mark Craven,
Ken DeJong, Jerry DeJong, Tom Dietterich, Susan Epstein, Oren Etzioni,
Scott Fahlman, Stephanie Forrest, David Haussler, Haym
Hirsh, Rob Holte,
Leslie Pack Kaelbling, Dennis Kibler, Moshe Koppel, John Koza, Miroslav
Kubat, John Lafferty, Ramon Lopez de Mantaras, Sridhar Mahadevan, Stan
Matwin, Andrew McCallum, Raymond Mooney, Andrew Moore, Katharina
Morik, Steve Muggleton, Michael Pazzani, David Poole, Armand Prieditis,
Jim Reggia, Stuart Russell, Lorenza Saitta, Claude Sammut, Jeff Schneider,
Jude
Shavlik, Devika Subramanian, Michael Swain, Gheorgh Tecuci, Se-
bastian Thrun, Peter Turney, Paul Utgoff, Manuela Veloso, Alex Waibel,
Stefan Wrobel, and Yiming Yang.
I
am also grateful to the many instructors and students at various universi-
ties who have field tested various drafts of this book and who have contributed
their suggestions. Although there is no space to thank the hundreds of students,
instructors, and others who tested earlier drafts of this book,
I
would like to thank
the following for particularly helpful comments and discussions:
Shumeet Baluja, Andrew Banas, Andy Barto, Jim Blackson, Justin Boyan,
Rich Caruana, Philip Chan, Jonathan Cheyer, Lonnie Chrisman, Dayne Frei-
tag, Geoff Gordon, Warren Greiff, Alexander
Harm, Tom Ioerger, Thorsten
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