Introduction to Artificial Intelligence 2nd edition by Wolfgang Ertel, Nathanael Black ISBN 3319584863 978-3319584867

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ISBN 10:  3319584863
ISBN 13: 978-3319584867
Author: Wolfgang Ertel, Nathanael Black 

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.

Introduction to Artificial Intelligence 2nd Table of contents:

• Introduction to Artificial Intelligence

  • Definition and history of artificial intelligence
  • Importance and applications of AI in modern technology
  • The relationship between AI, machine learning, and data science

• Intelligent Agents and Problem Solving

  • Concept of intelligent agents
  • Problem-solving techniques in AI (search algorithms, state spaces)
  • Problem-solving strategies (uninformed and informed search)

• Knowledge Representation and Reasoning

  • Types of knowledge representation (logic, frames, semantic networks)
  • Deductive and inductive reasoning
  • Knowledge representation languages (predicate logic, production rules)

• Search Algorithms and Optimization

  • Uninformed search techniques (breadth-first, depth-first search)
  • Informed search (A*, greedy search, heuristics)
  • Optimization problems and methods (genetic algorithms, simulated annealing)

• Machine Learning and Data Mining

  • Basics of machine learning (supervised, unsupervised learning)
  • Learning algorithms (decision trees, k-nearest neighbors, SVM)
  • Neural networks and deep learning fundamentals
  • Introduction to data mining and its applications

• Natural Language Processing

  • Overview of NLP and its challenges
  • Text analysis and representation (bag of words, TF-IDF)
  • Syntax, semantics, and parsing
  • Applications of NLP (chatbots, sentiment analysis, machine translation)

• Computer Vision and Image Processing

  • Basics of computer vision and image recognition
  • Feature extraction and object detection
  • Image classification techniques and deep learning applications
  • Applications in real-world systems (autonomous vehicles, facial recognition)

• Expert Systems and Rule-Based Systems

  • Concept of expert systems and their components
  • Knowledge base, inference engine, and user interface
  • Rule-based reasoning and decision support systems
  • Case studies and applications

• Robotics and AI

  • Robotics and its intersection with AI
  • Robot perception, navigation, and control
  • Planning and decision-making in robotics
  • Human-robot interaction and autonomous systems

• Ethical and Social Implications of AI

  • Ethical issues in AI (privacy, bias, fairness)
  • Impact of AI on jobs and society
  • Future challenges in AI development and governance

• AI in the Real World

  • AI applications in various industries (healthcare, finance, entertainment)
  • Challenges in deploying AI systems in real-world scenarios
  • Future trends and research directions in AI

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