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27.8 MB
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Oct. 15, 2025, 12:32 p.m.
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(Last updated: Oct. 15, 2025, 12:34 p.m.)
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| Kuznetsov O. Intelligent Systems. From Theory to Applications...2026.pdf | 27.8 MB |
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SOURCE: Kuznetsov O. Intelligent Systems. From Theory to Applications...2026
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MEDIAINFO
Textbook in PDF format 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 book’s structure reflects a thoughtful progression through the essential components of intelligent systems. It begins with fundamental concepts that provide a solid theoretical foundation, then advances through increasingly sophisticated topics: • The introductory chapters establish a clear framework for understanding what intelligent systems are and how they function, making these complex concepts accessible even to those new to the field. • The sections on search algorithms—from basic uninformed search to advanced heuristic methods—demonstrate how computational problems can be efficiently solved through systematic exploration of possibility spaces. • The chapters on optimization techniques, including genetic algorithms and simulated annealing, show how nature-inspired approaches can tackle problems traditional algorithms struggle with. • The Machine Learning foundations provided in the later chapters prepare students for the rapidly evolving AI landscape, with practical implementations that demystify these powerful techniques. What truly distinguishes this book is its commitment to practical application. Each theoretical concept is immediately reinforced with Python code that students can run, modify, and extend. This hands-on approach transforms passive learning into active skill-building, allowing students to develop confidence in their ability to implement intelligent systems. 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. About This Book • Conceptual Introduction: Clear explanations of key ideas with minimal techni cal jargon. • Theoretical Framework: Mathematical foundations presented in an accessi ble manner. • Practical Implementation: Python code examples demonstrating how to apply concepts. • Exercises: Problems ranging from basic to advanced to reinforce learning. • Case Studies: Real-world applications that illustrate practical relevance. Readers will benefit most from this book with: • Basic programming knowledge, particularly in Python • Foundational understanding of mathematics (algebra, calculus, probability) • Familiarity with fundamental computer science concepts No prior knowledge of artificial intelligence or machine learning is required. Necessary concepts are introduced as they appear. Supporting Materials All code examples are available online through the companion website. Additional resources include: • Solution guides for selected exercises • Supplementary datasets for practice • Updated examples reflecting current technologies • Links to relevant research papers Preface Introduction to Intelligent Systems The Evolution of Artificial Intelligence The Turing Test and Fundamental AI Concepts Modern Applications of Intelligent Systems Problem Formulation and Search Spaces Uninformed Search Algorithms Informed Search Algorithms The A* Algorithm Genetic Algorithms Hill Climbing Simulated Annealing. Gradient-Based Optimization Tabu Search Swarm Intelligence Introduction to Machine Learning Supervised Learning
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