Advanced Intelligence Methods for Data Science and Optimization
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Product details:
- Publisher Elsevier Science
- Date of Publication 1 September 2026
- ISBN 9780443289408
- Binding Paperback
- No. of pages250 pages
- Size 235x191 mm
- Weight 450 g
- Language English 0
Categories
Long 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.
Table of Contents:
1. Introduction to Deep Learning: Concepts, Applications, and Challenges
2. Evolutionary Optimization Techniques: Principles, Algorithms, and Real-World Applications
3. Reinforcement Learning for Decision Making in Complex Environments
4. Natural Language Processing: Techniques and Applications in Text Mining
5. Time Series Forecasting: Methods and Evaluation Metrics
6. Multi-Objective Optimization for Real-World Decision Making
7. Advanced Analytics for Large-Scale Data: Techniques and Tools
8. Image and Video Processing using Deep Learning: Applications and Challenges
9. Bayesian Optimization: Methods and Applications
10. Fuzzy Logic and its Applications in Data Science and Optimization
11. Quantum Computing for Data Science: Principles and Applications
12. Swarm Intelligence: Models, Algorithms, and Applications
13. Graph Theory and its Applications in Data Science and Optimization
14. Convex Optimization: Theory and Algorithms
15. Game Theory and its Applications in Data Science and Optimization
16. Clustering Techniques for Big Data: Methods and Applications
17. Anomaly Detection Techniques: Principles, Algorithms, and Applications
18. Differential Evolution: Principles, Variants, and Applications
19. Robust Optimization: Theory, Methods, and Applications
20. Neural Architecture Search: Concepts, Techniques, and Challenges
