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Introduction to Artificial Intelligence (2nd Edition)

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Springer

Pages: 356
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Paperback - 9783319584867

29 January 2018

$69.99

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Ebook - 9783319584874

18 January 2018

$54.99

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This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical...

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This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning.

Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW).

Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.

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An ideal, quick resource on A.I., excellent for self-study

Presents an application-focused and hands-on approach to learning the subject

Provides study exercises at the end of each chapter, in addition to highlighted examples, definitions, theorems, and illustrative cartoons

Updated second edition featuring new material on deep learning

Introduction
Propositional Logic
First-order Predicate Logic
Limitations of Logic
Logic Programming with PROLOG
Search, Games and Problem Solving
Reasoning with Uncertainty
Machine Learning and Data Mining
Neural Networks
Reinforcement Learning
Solutions for the Exercises.

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Dr. Wolfgang Ertel is a professor at the Institute for Artificial Intelligence at the Ravensburg-Weingarten University of Applied Sciences, Germany.

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Dr. Wolfgang Ertel is a professor at the Institute for Artificial Intelligence at the Ravensburg-Weingarten University of Applied Sciences, Germany.

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