Machine learning paradigms is an excellent introduction to both immunology from a computer scientist point of view and to algorithms designed and inspired by the biological immune system.

The first part of the book is rather traditional, covering the conventional topics in a typical machine learning textbook. The second part of the book is the most interesting. It starts with the biological immune system, providing an excellent introduction to the biological (and, of course, original) side. This is followed by a section on the artificial side of immune-oriented machine learning, covering a wide range of applications to problems of classification and learning.

This is definitely a book for people interested in the topic, both as a reference and as an in-depth look at some of the most exciting research directions, in particular that of software personalization in which immune-based computing is meant to make its greatest contribution by the way in which it is highly adaptable. The book includes both general discussions and highly technical mathematics, so people with different backgrounds will be able to enjoy its contents.