David Forsyth - Applied Machine Learning

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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 hadnt 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 arent necessarily going to want to be machine learning researchersas 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 its different from what one sees in other books.

The book is revised and corrected from class notes written for a course Ive 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 werent 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 its 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 thats 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 Ive covered enough to get you started and to get you to realize that its 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 shouldnt know less.

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, always programming assignments. Different instructors or readers may have different needs, and so I sketch some pointers to what can be omitted below.

General Background : Your linear algebra should be reasonably fluent at a practical level. Fairly soon, we will see matrices, vectors, orthonormal matrices, eigenvalues, eigenvectors, and the singular value decomposition. All of these ideas will be used without too much comment.

Programming Background : You should be able to pick up a practical grasp of a programming environment without too much fuss. I use either R or MATLAB for this sort of thing, depending on how reliable I think a particular package is. You should, too. In some places, its a good idea to use Python.

Survival Skills : Most simple questions about programming can be answered by searching. I usually use a web search to supply details of syntax, particular packages, etc. which I forget easily. You should, too. When someone asks me, say, how do I write a loop in R? in office hours, I very often answer by searching for R loop (or whatever) and then pointing out that Im not actually needed. The questioner is often embarrassed at this point. You could save everyone trouble and embarrassment by cutting out the middleman in this transaction.

I think using real datasets is an important feature of this book. But real life is messy, and datasets I refer to here may have been moved by the time you read this. Generally, a little poking around using a web search engine will find datasets or items that have been moved. I will also try to provide pointers to missing or moved datasets on a webpage which will also depend from my home page. Its easy to find me on the Internet by searching for my name, and ignoring the soap opera star (thats really not me).

Generally, I have followed the natural style of a textbook and tried not to cite papers in the text. Im not up to providing a complete bibliography of modern machine learning and didnt want to provide an incomplete one. However, I have mentioned papers in some places and have done so when it seemed very important for users to be aware of the paper or when I was using a dataset whose compilers asked for a citation. I will try to correct errors or omissions in citation on a webpage, which will depend from my home page.