Fundamentals of Pattern Recognition and Machine Learning

This is an introductory book on Pattern Recognition, including Machine Learning methods, which is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduateor advanced undergraduate level. It can also be used for self-study or as a supplemental text for courses in Machine Learning, Bioinformatics, and Materials Informatics.
The purpose of the book is to be concise but thorough: the text avoids being verbose; it is selective, meaning that it is not an encyclopedic approach as seen in some other textbooks. Despite this, it covers all topics commonly used in prediction from data, including classification, feature selection, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks.

Mathematically rigorous: does not shy away from mathematically sophisticated topics; contains mathematical notation, and theorems and proofs where required. This is balanced with a practical perspective and computer projects.

The book contains a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification and Bayesian error estimation, and distributional theory. It also presents a balanced approach between advanced mathematics and practical application. Every chapter ends with an exercise section and a computer projects section. The computer projections use data from real Bioinformatics and Materials Informatics applications.