AHA-BUCH

Introduction to Artificial Intelligence

Originaltitel:Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung
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ISBN-13:
9783319584867
Einband:
Book
Erscheinungsdatum:
01.02.2018
Seiten:
356
Autor:
Wolfgang Ertel
Gewicht:
626 g
Format:
236x154x22 mm
Serie:
Undergraduate Topics in Computer Science
Sprache:
Englisch
Beschreibung:

This accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. It provides study exercises at the end of each chapter, plus examples, definitions, theorems, and illustrations.
An ideal, quick resource on A.I., excellent for self-study
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
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.