New Trends in Machine Learning

By: Amama, Clement Major

This book provides a comprehensive and accessible overview of emerging trends in Machine Learning (ML), highlighting the transformation of the field from traditional algorithm-focused approaches to a broader systems-oriented discipline. It examines key paradigms such as Federated Learning, Explainable Artificial Intelligence, Graph Neural Networks, Self-Supervised and Transfer Learning, AutoML, TinyML, Quantum Machine Learning, Reinforcement Learning, and Multimodal Learning.Beyond technical foundations, the book integrates an empirical analysis of recent research to reveal how modern ML is increasingly shaped by concerns such as privacy, interpretability, scalability, energy efficiency, and governance. It explores real-world applications across sectors, including healthcare, finance, transportation, and cybersecurity, and addresses ethical and societal implications.By combining conceptual explanations with research-driven insights, this book offers a structured understanding of both current developments and future directions in machine learning, making it a valuable resource for students, researchers, and practitioners navigating the evolving ML landscape.

• Format: Paperback
• Language: English
• ISBN: 6209793657
• ISBN13: 9786209793653
• Release Date: March 2026
• Publisher: Scholars' Press
• Length: 56 Pages
• Weight: 0.19 lbs.
• Dimensions: 0.1" x 6.0" x 9.0"