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.