Statistical Foundations of Machine Learning: The Handbook

We are in the era of big data. There are essentially two reasons why people gather increasing volumes of data: first, they think some valuable assets are implicitly coded within them, and second computer technology enables effective data storage at reduced costs.

The procedure for finding useful patterns in data is known by different names in different communities but more and more, the set of computational techniques and tools to support the modelling of large amount of data is grouped under the label of machine learning.

This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. In particular, we focus on supervised learning problems, where the goal is to model the relation between a set of input variables, and one or more output variables, which are considered to be dependent on the inputs in some manner.