データサイエンスと最適化のための革新知能手法 Advanced Intelligence Methods for Data Science and Optimization

著者名：Gandomi, Amir Hossein (EDT)/Mirjalili PhD, Seyedali (EDT)/Kovacs, Levente (EDT)

Morgan Kaufmann（2026/09/03発売）

• 言語：ENG
• ISBN：9780443289408
• eISBN：9780443289415

Description

Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains.It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.- Provides comprehensive coverage of advanced intelligence methods- Includes real-world examples and case studies that illustrate the application of these methods across a wide range of fields- Begins with an introduction to Deep Learning concepts and quickly moves to the most leading-edge topics in computational intelligence, all with an application to data science techniques

Table of Contents

1. Introduction to Deep Learning: Concepts, Applications, and Challenges2. Evolutionary Optimization Techniques: Principles, Algorithms, and Real-World Applications3. Reinforcement Learning for Decision Making in Complex Environments4. Natural Language Processing: Techniques and Applications in Text Mining5. Time Series Forecasting: Methods and Evaluation Metrics6. Multi-Objective Optimization for Real-World Decision Making7. Advanced Analytics for Large-Scale Data: Techniques and Tools8. Image and Video Processing using Deep Learning: Applications and Challenges9. Bayesian Optimization: Methods and Applications10. Fuzzy Logic and its Applications in Data Science and Optimization11. Quantum Computing for Data Science: Principles and Applications12. Swarm Intelligence: Models, Algorithms, and Applications13. Graph Theory and its Applications in Data Science and Optimization14. Convex Optimization: Theory and Algorithms15. Game Theory and its Applications in Data Science and Optimization16. Clustering Techniques for Big Data: Methods and Applications17. Anomaly Detection Techniques: Principles, Algorithms, and Applications18. Differential Evolution: Principles, Variants, and Applications19. Robust Optimization: Theory, Methods, and Applications20. Neural Architecture Search: Concepts, Techniques, and Challenges