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Beginning artificial intelligence with the Raspberry Pi
Norris D., Apress, New York, NY, 2017. 369 pp. Type: Book (978-1-484227-42-8)
Date Reviewed: May 11 2018

For anybody looking for a fast and basic introduction to the field of artificial intelligence (AI) or machine learning (ML) supported by many practical examples for one’s own implementation, the book by Norris is a perfect match. Although the author truly and honestly ensures that a reader will not become a specialist after finishing the book, getting acquainted with this position is really fruitful, since quite a lot of interesting material is given. The ideas are shown with working software for individual practice and development; therefore, it is possible to instantly practice the knowledge acquired.

The book presents a broad review of various classical flavors of AI/ML (expert systems, decision trees, fuzzy logic, neural networks, and evolutionary computing), supported with examples of different programming language exercises (Prolog and mainly Python are used). Moreover, some of the examples involve not only pure interaction with the screen, but also with manipulation of physical objects, such as an LCD display, a camera, or a robotic car. The tangibility and liveliness of such examples shows the importance and usefulness of the presented field in an impactful way. This approach also increases the interest of a reader, who (just after reading this) is likely to be willing to jump into more dense and theoretical works on AI/ML (for example, [1,2]) that, while being informative for a specialist, are typically not easy to understand for an undergraduate. Indeed, Norris’s book may be used even by laymen in AI/ML since the ideas are introduced as if the reader has only the everyman’s comprehension of the ideas and is drawn to the topic by public information on AI’s successes, such as its ability to beat humans in chess or Go. On the other hand, while this is an important advantage, basic skills in operating systems or programming are necessary to fully gain from this work.

Except for introducing the generalities of the covered field, the book is also a useful source of inspiration for a teacher preparing laboratory or project classes in AI/ML. Hence, this contribution can be used as a manual (versus a regular textbook) for such classes. Additionally, Raspberry Pi and the related free software, which appears to be surprisingly powerful, are promoted. This aspect should not be neglected because it is an important advantage of the book, as it extends the idea of programming and benefiting from the achievements of computer science to everybody.

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Reviewer:  Piotr Cholda Review #: CR146028 (1807-0370)
1) Watt, J.; Borhani, R.; Katsaggelos, A. K. Machine learning refined. Cambridge University Press, Cambridge, UK, 2016.
2) Gori, M. Machine learning: a constraint-based approach. Morgan Kaufmann, Cambridge, MA, 2017.
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