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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
227
14 s/w Abbildungen, 227 farbige Abbildungen
XXIX, 395 p. 241 illus., 227 illus. in color.
ISBN-13
978-3-032-00044-6 (9783032000446)
Schweitzer Klassifikation
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.
