Fundamentals of Robust Machine Learning

The reliability and stability of machine learning systems are crucial in real-world applications, where data variability, noise, and uncertainty can significantly affect model performance. Building models that remain effective under such challenges defines the essence of robust AI. Fundamentals of Robust Machine Learning explores the theoretical and practical approaches to designing resilient learning algorithms. The book discusses adversarial robustness, data augmentation, uncertainty quantification, and generalization techniques. It also covers robust optimization and fairness in model evaluation. Combining mathematical rigor with applied examples, it provides students, researchers, and engineers with tools to build dependable machine learning systems capable of handling complex, imperfect, and evolving data environments.

Bechoo Lal, PhD. became a Member (M) of IAENG: International Association of Engineers, USA with membership (108820) in 2010, a Senior Member (SM) in 2019. I am doctorate PhD in Computer Science, PhD- Information System from University of Mumbai, M.Tech-CSE, Master of Computer Application (MCA) - Banaras Hindu University (BHU), India, and PGP- Data Science from Purdue University, USA. Currently I am working as an Associate Professor in Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation (KLEF) - KL University Vijayawada Campus Andhra Pradesh, India. In addition to this I am supervising PhD research scholars from SJJT University, Rajasthan, India. My research areas are data science, big data analytics and Machine Learning.