Preface
Machine learning methods are now an important tool for scientists, researchers,
engineers, and students in a wide range of areas. Many years ago, one could publish
papers introducing (say) classifiers to one of the many fields that hadn’t heard of
them. Now, you need to know what a classifier is to get started in most fields. This
book is written for people who want to adopt and use the main tools of machine
learning but aren’t necessarily going to want to be machine learning researchers—as
of writing, this seems like almost everyone. There is no new fact about machine
learning here, but the selection of topics is my own. I think it’s different from what
one sees in other books.
The book is revised and corrected from class notes written for a course I’ve
taught on numerous occasions to very broad audiences. Most students were at the
final year undergraduate/first year graduate student level in a US university. About
half of each class consisted of students who weren’t computer science students but
still needed a background in learning methods. The course stressed applying a wide
range of methods to real datasets, and the book does so, too.
The key principle in choosing what to write about was to cover the ideas in
machine learning that I thought everyone who was going to use learning tools should
have seen, whatever their chosen specialty or career. Although it’s never a good
thing to be ignorant of anything, an author must choose. Most people will find a
broad shallow grasp of this field more useful than a deep and narrow grasp, so this
book is broad, and coverage of many areas is shallow. I think that’s fine, because
my purpose is to ensure that all have seen enough to know that, say, firing up a
classification package will make many problems go away. So I’ve covered enough to
get you started and to get you to realize that it’s worth knowing more.
The notes I wrote have been useful to more experienced students as well. In
my experience, many learned some or all of this material without realizing how
useful it was and then forgot it. If this happened to you, I hope the book is a
stimulus to your memory. You really should have a grasp of all of this material.
You might need to know more, but you certainly shouldn’t know less.
This Book
I wrote this book to be taught, or read, by starting at the beginning and proceeding
to the end. In a 15-week semester, I cover a lot and usually set 12 assignments, al-
ways programming assignments. Different instructors or readers may have different
needs, and so I sketch some pointers to what can be omitted below.
What You Need to Know Before You Start
This book assumes you have a moderate background in probability and statistics
before you start. I wrote a companion book, Probability and Statistics for Com-
puter Science, which covers this background. There is a little overlap, because not
everyone will read both books cover to cover (a mistake—you should!). But I’ve
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