A First Encounter with Machine Learning 2010

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.