Machine learning is a broad and fascinating field. It has been called one of the sexiest fields to work in. It has applications in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Its importance is likely to grow, as more and more areas turn to it as a way of dealing with the massive amounts of data available.

The purpose of this book is to provide a gentle and pedagogically organized introduction to the field. This is in contrast to most existing machine learning texts, which tend to organize things topically, rather than pedagogically (an exception is Mitchell’s book, but unfortunately that is getting more and more outdated). This makes sense for researchers in the field, but less sense for learners. A second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. It is not possible (or even advisable) to avoid math. But math should be there to aid understanding, not hinder it. Finally, this book attempts to have minimal dependencies, so that one can fairly easily pick and choose chapters to read. When dependencies exist, they are listed at the start of the chapter, as well as the list of dependencies at the end of this chapter.

The audience of this book is anyone who knows differential calculus and discrete math, and can program reasonably well. (A little bit of linear algebra and probability will not hurt). An undergraduate in their fourth or fifth semester should be fully capable of understanding this material. However, it should also be suitable for first year graduate students, perhaps at a slightly faster pace.