Introduction to Artificial Intelligence (Undergraduate Topics in Computer Science) - Softcover

Ertel, Wolfgang

Softcover

ISBN 10: 0857292986 ISBN 13: 9780857292988

Verlag: Springer, 2011

Inhaltsangabe

Book by Ertel Wolfgang

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Críticas:

“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)

Reseña del editor:

This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website.

Über diese Auflage

• Verlag: Springer
• Erscheinungsdatum: 2011
• ISBN 10: 0857292986
• ISBN 13: 9780857292988
• Einband: Tapa blanda
• Anzahl der Seiten: 330
• Zeichner: Mast Florian