In winter quarter 2007 I taught an undergraduate course in machine learning at UC Irvine. While I had been teaching machine learning at a graduate level it became soon clear that teaching the same material to an undergraduate class was a whole new challenge. Much of machine learning is build upon concepts from mathematics such as partial derivatives, eigenvalue decompositions, multivariate probability densities and so on. I quickly found that these concepts could not be taken for granted at an undergraduate level. The situation was aggravated by the lack of a suitable textbook. Excellent textbooks do exist for this field, but I found all of them to be too technical for a first encounter with machine learning.

This experience led me to believe there was a genuine need for a simple, intuitive introduction into the concepts of machine learning. A first read to wet the appetite so to speak, a prelude to the more technical and advanced textbooks. Hence, the book you see before you is meant for those starting out in the field who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer.

Machine learning is a relatively recent discipline that emerged from the general field of artificial intelligence only quite recently. To build intelligent machines researchers realized that these machines should learn from and adapt to their environment. It is simply too costly and impractical to design intelligent systems by first gathering all the expert knowledge ourselves and then hard-wiring it into a machine. For instance, after many years of intense research the we can now recognize faces in images to a high degree accuracy.