The field of Artificial Intelligence has seen explosive growth in recent years, yet a persistent challenge remains, namely bridging the gap between theoretical concepts and practical implementation. Too often, students encounter either highly abstract mathematical treatments disconnected from real-world applications, or simplified implementations that fail to convey the underlying principles. This textbook directly addresses this challenge through its unique approach combining clear theoretical explanations with comprehensive Python implementations.

Drawing from the author's extensive experience teaching at the University of eCampus, Italy, this book provides a thorough exploration of intelligent systems, covering classical approaches to cutting-edge techniques. Organized into three main areas, the book explores the foundations of intelligent systems, examines optimization and search methods that form the backbone of AI solutions, and ends by investigating machine learning fundamentals that enable systems to derive knowledge from experience. A distinguishing feature of this work is its practical approach. Each theoretical concept is paired with Python implementations and exercises. This hands-on methodology develops both conceptual understanding and practical skills simultaneously. The exercises progress from basic implementations to complex real-world problems.

The textbook aims to serve both undergraduate and graduate students in computer science, engineering, and related disciplines. It assumes basic programming knowledge but introduces concepts progressively. Professionals implementing intelligent systems will also find valuable insights and practical guidance. Despite AI's rapid evolution, this book provides both current knowledge and the conceptual framework necessary for understanding future developments. Ethical considerations are addressed throughout, encouraging critical thinking about responsible AI implementation. It is the author's hope that this book will be a valuable resource in the reader's journey to understand and design intelligent systems.

Comprehensively bridges the gap between theory and practical implementations in the field of Artificial Intelligence Provides a thorough exploration of intelligent systems, covering classical approaches and cutting-edge techniques Is ideal for undergraduate and graduate students in computer science, engineering, cognitive science or robotics Request lecturer material: sn.pub/lecturer-material

Autorentext

Prof. Oleksandr Kuznetsov is a faculty member at the Department of Theoretical and Applied Sciences, eCampus University, Italy. He also works as a Senior Data Scientist at Proxima Labs in San Francisco, USA. Prof. Kuznetsov has extensive experience in teaching and researching intelligent systems, with a focus on bridging theoretical concepts with practical applications. He has developed and taught courses on Artificial Intelligence, Machine Learning, and Intelligent Systems at the university level, and has published numerous papers in peer-reviewed journals and conferences in these fields.

Inhalt

"Dedication.- Acknowledgments.- Foreword.- Preface.- About This Book.- Acronyms.- 1".- " Introduction to Intelligent Systems".- "2. The Evolution of Artificial Intelligence".- "3. The Turing Test and Fundamental AI Concepts".- "4. Modern Applications of Intelligent Systems".- "5. Problem Formulation and Search Spaces".- "6. Uninformed Search Algorithms".- "7. Informed Search Algorithms".- "8. The A Algorithm".- "9. Genetic Algorithms".- "10. Hill Climbing".- "11. Simulated Annealing".- "12. Gradient-Based Optimization".- "13. Tabu Search.- "14. Swarm Intelligence".- "Part III. Advanced Machine Learning".- "15. Introduction to Machine Learning".- "16. Supervised Learning".- "17. Unsupervised Learning".- "18. Reinforcement Learning.- Appendix A: Uninformed Search Algorithm Exercises.- Appendix B: Informed Search Algorithm Exercises.- Appendix C: A Algorithm Implementation Exercises.- Appendix D: Genetic Algorithms Exercises.- Appendix E: Hill Climbing Exercises.- Appendix F: Simulated Annealing Exercises.- Appendix G: Gradient Descent Optimization Exercises.- Appendix H: Tabu Search Exercises.- Appendix I: Swarm Intelligence Exercises.- Appendix J: Machine Learning Fundamentals Exercises.- Appendix K: Supervised Learning Exercises.- Appendix L: Unsupervised Learning Exercises.- Appendix M: Reinforcement Learning Exercises".