Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same ﬁeld, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had signiﬁcant impact on both algorithms and applications.
This new textbook reﬂects these recent developments while providing a compre- hensive introduction to the ﬁelds of pattern recognition and machine learning. It is aimed at advanced undergraduates or ﬁrst year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or ma- chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not es- sential as the book includes a self-contained introduction to basic probability theory. Because this book has broad scope, it is impossible to provide a complete list of references, and in particular no attempt has been made to provide accurate historical attribution of ideas. Instead, the aim has been to give references that offer greater detail than is possible here and that hopefully provide entry points into what, in some cases, is a very extensive literature. For this reason, the references are often to more recent textbooks and review articles rather than to original sources.