Engineering Mathematics and Artificial Intelligence
eBook - ePub

Engineering Mathematics and Artificial Intelligence

Foundations, Methods, and Applications

  1. 513 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Engineering Mathematics and Artificial Intelligence

Foundations, Methods, and Applications

About this book

The 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.

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Yes, you can access Engineering Mathematics and Artificial Intelligence by Herb Kunze, Davide La Torre, Adam Riccoboni, Manuel Ruiz Galán, Herb Kunze,Davide La Torre,Adam Riccoboni,Manuel Ruiz Galán in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Game Theory. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. Editors
  9. Contributors
  10. Chapter 1 Multiobjective Optimization: An Overview
  11. Chapter 2 Inverse Problems
  12. Chapter 3 Decision Tree for Classification and Forecasting
  13. Chapter 4 A Review of Choice Topics in Quantum Computing and Some Connections with Machine Learning
  14. Chapter 5 Sparse Models for Machine Learning
  15. Chapter 6 Interpretability in Machine Learning
  16. Chapter 7 Big Data: Concepts, Techniques, and Considerations
  17. Chapter 8 A Machine of Many Faces: On the Issue of Interface in Artificial Intelligence and Tools from User Experience
  18. Chapter 9 Artificial Intelligence Technologies and Platforms
  19. Chapter 10 Artificial Neural Networks
  20. Chapter 11 Multicriteria Optimization in Deep Learning
  21. Chapter 12 Natural Language Processing: Current Methods and Challenges
  22. Chapter 13 AI and Imaging in Remote Sensing
  23. Chapter 14 AI in Agriculture
  24. Chapter 15 AI and Cancer Imaging
  25. Chapter 16 AI in Ecommerce: From Amazon and TikTok, GPT-3 and LaMDA, to the Metaverse and Beyond
  26. Chapter 17 The Difficulties of Clinical NLP
  27. Chapter 18 Inclusive Green Growth in OECD Countries: Insight from the Lasso Regularization and Inferential Techniques
  28. Chapter 19 Quality Assessment of Medical Images
  29. Chapter 20 Securing Machine Learning Models: Notions and Open Issues
  30. Index