Description
Book SynopsisThe fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams.
Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book.
This book is written for researchers, practitioners, engineers, and AI consultants.
Table of Contents1. Multiobjective Optimization: An Overview. 2. Inverse Problems. 3. Decision Tree for Classification and Forecasting. 4. A Review of Choice Topics in Quantum Computing and Some Connections with Machine Learning. 5. Sparse Models for Machine Learning. 6. Interpretability in Machine Learning. 7. Big Data: Concepts, Techniques, and Considerations. 8. A Machine of Many Faces: On the Issue of Interface in Artificial Intelligence and Tools from User Experience. 9. Artificial Intelligence Technologies and Platforms. 10. Artificial Neural Networks. 11. Multicriteria Optimization in Deep Learning. 12. Natural Language Processing: Current Methods and Challenges. 13. AI and Imaging in Remote Sensing. 14. AI in Agriculture. 15. AI and Cancer Imaging. 16. AI in Ecommerce: From Amazon and TikTok, GPT-3 and LaMDA, to the Metaverse and Beyond. 17. The Difficulties of Clinical NLP. 18. Inclusive Green Growth in OECD Countries: Insight from The Lasso Regularization and Inferential Techniques. 19. Quality Assessment of Medical Images. 20. Securing Machine Learning Models: Notions and Open Issues.
