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 propagation.
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 comprehensive
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 machine
learning concepts. Knowledge of multivariate calculus and basic linear algebra
is required, and some familiarity with probabilities would be helpful though not essential
as the book includes a self-contained introduction to basic probability theory.