Pattern Recognition and Machine Learning

Citation

Howard, W.R. (2007), "Pattern Recognition and Machine Learning", Kybernetes, Vol. 36 No. 2, pp. 275-275. https://doi.org/10.1108/03684920710743466

This book appears in the Information Science and Statistics Series commissioned by the publishers.

In this text, no previous knowledge of pattern recognition or of machine learning is necessary. The book appears to have been designed for course teaching, but obviously contains material that readers interested in self‐study can use. It is certainly structured for easy use.

These are subjects which both cyberneticians and systemists are particularly interested in and, indeed, cybernetics has contributed generously to the development of both.

It should be noted that here, pattern recognition is introduced from the Bayesian point of view. Included are approximate inference algorithms that produce quick approximate answers in some situations where exact ones are not possible.

The publishers itemise the courses they believe it is suitable to be used with. They include: machine learning; statistics; computer science; signal processing; computer vision; data mining; bioinformatics.

For course teachers there is ample backing which includes some 400 exercises. Although not designed for general reading it does contain important material which can be easily followed without the reader being confined to a pre‐determined course of study.