Engineering Mathematics and Artificial Intelligence

Herb Kunze is a Professor of Mathematics at the University of Guelph, in Guelph, Ontario, Canada. He received his Ph.D. in Applied Mathematics from the University of Waterloo in 1997. He has held research funding from the Natural Sciences and Engineering Research Council (NSERC) throughout his career. Among his research interests are fractal-based methods in analysis, including a wide array of both direct and inverse problems; neural networks and artificial intelligence; mathematical imaging; and qualitative properties of differential equations. His work combines rigorous theoretical elements with application-driven considerations. He has over 100 research publications, generally in high-impact, refereed journals.

The fields of Artificial Intelligence and Machine Learning 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.

1. 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.