Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications

✍ Scribed by Herb Kunze, Davide La Torre, Adam Riccoboni, Manuel Ruiz Galán

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 530
• Series: Mathematics and its Applications
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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