“ The book overall is very readable and relevant. One of the most valuable aspects of this book are the worked out examples and numerous (solved) exercises. Overall, this is a very well written and pedagogical book that fills an important niche in the Artificial Intelligence educational literature. Highly recommended.” ( Bojan Tunguz, tunguzreview.com, July, 2015)“ This accessible and concise introduction to the field of artificial intelligence ( AI) is intended primarily for self-study or as a foundation of a short course on the subject. The book consists of ten topic chapters, each one of which offers an extended list of exercises. Chapter 11 contains solutions to all exercises. Additional teaching resources, including lecture slides, are available on the book website.” ( Neli Zlatareva, Zentralblatt MATH, Vol. 1238, 2012)“ The book is aimed primarily at undergraduates who have not yet taken linear algebra or multidimensional calculus. it contains many exercises with solutions at the back; thus, it supports self-learning. The many excellent figures, some in color, help make the material easily understandable. A companion Web site contains supplementary materials, such as program code for the book, most of which is in or commented on in German. Summing Up: Recommended. Upper-division undergraduates and above.” ( S. L. Tanimoto, Choice, Vol. 49 (2 October, 2011).
Learning Deep Architectures for AI ( Yoshua Bengio) This book discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed. Logic Programming in Scheme ( Nils M. Holm) Questions answered in this little book: What is logic programming? Why is negation hard in logic programming? What is cutting? How do I solve logic puzzles? How is logic programming implemented? Expert Systems in Prolog ( Dennis Merritt) For Prolog programmer interested in either building expert systems or experimenting with various expert system techniques. using a step-by-step approach to building systems, explaining the concepts and showing the Prolog code at each stage. Planning Algorithms ( Steven M. La Valle) This is the only book for teaching and referencing of Planning Algorithms in applications including robotics, computational biology, computer graphics, manufacturing, aerospace applications and medicine, etc. Genetic Algorithms in Applications ( Rustem Popa) This well-organized book takes the reader through the new and rapidly expanding field of genetic algorithms step by step, from a discussion of numerical optimization, to a survey of current extensions to genetic algorithms and applications. Conceptive C ( Harry Mc Geough) Conceptive C is an AI programming Language based on Objective- C and C Language. It is a superset of both languages and is designed to work with Apple Mac OS X and i OS. Essentials of Metaheuristics ( Sean Luke) This book is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts. Machine Learning Using C Succinctly ( James Mc Caffrey) This book shows several different approaches to applying machine learning to data.
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