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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 wit… Altro …

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The ultimate aim of artificial intelligence (A.I.) is to understand intelligence and to build intelligent software and robots that come close to the performance of humans. On their way to… Altro …

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Introduction to Artificial Intelligence - Nathanael T. Black, Wolfgang Ertel
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Introduction to Artificial Intelligence - nuovo libro

ISBN: 9783319584874

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 wit… Altro …

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Introduction to Artificial Intelligence - nuovo libro

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Informazioni dettagliate del libro - Introduction to Artificial Intelligence


EAN (ISBN-13): 9783319584874
Anno di pubblicazione: 2018
Editore: Springer International Publishing

Libro nella banca dati dal 2018-01-24T11:26:20+01:00 (Zurich)
Pagina di dettaglio ultima modifica in 2024-02-24T20:43:12+01:00 (Zurich)
ISBN/EAN: 9783319584874

ISBN - Stili di scrittura alternativi:
978-3-319-58487-4
Stili di scrittura alternativi e concetti di ricerca simili:
Autore del libro : springer, wolfgang ertel, wolf
Titolo del libro: artificial intelligence


Dati dell'editore

Autore: Wolfgang Ertel
Titolo: Undergraduate Topics in Computer Science; Introduction to Artificial Intelligence; Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung
Editore: Springer; Springer International Publishing
356 Pagine
Anno di pubblicazione: 2018-01-18
Cham; CH
Traduttore: Nathanael T. Black
Lingua: Inglese
69,54 € (DE)
71,50 € (AT)
77,00 CHF (CH)
Available
XIV, 356 p. 130 illus., 46 illus. in color.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Artificial Intelligence; Agents; Logic; Search; Reasoning with Uncertainty; Machine Learning; Neural Networks; B; Artificial Intelligence; Computer Science; BC

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 websiteContains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoonsIncludes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learningReports 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 tounderstand the material.Dr. Wolfgang Ertel is a professor at the Institute for Artificial Intelligence at the Ravensburg-Weingarten University of Applied Sciences, Germany.
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

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