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Book Synopsis

"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".

Intelligent Systems From Theory to Applications

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    A Hardback by Oleksandr Kuznetsov

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      Publisher: Springer
      Publication Date: Publication Date: 04/12/2025
      ISBN13: 9783032000439, 978-3032000439
      ISBN10:
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      Computing Artificial intelligence (AI)

      Description

      Book Synopsis

      "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".

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