Basics

Abstract

The goal of this chapter is to provide the reader with an overview of the machine learning techniques used in this book. A good introduction to the field of machine learning in general can be found in the books of Bishop (2007) and MacKay (2003). This chapter starts with a review of common machine learning techniques for regression, classification, dimensionality reduction, and clustering problems. To compare and rank alternative models, we present in Section 2.2 several measures to evaluate the quality of a model and to select the best one. Finally, we introduce in Section 2.3 Bayesian networks as a tool to factorize high-dimensional learning problems into independent components.