Reliable machine learning requires systems that excel despite noisy, uncertain, and evolving data. By combining adversarial defenses, data augmentation, uncertainty quantification, fairness, and robust optimization techniques, theoretical insights merge with practical examples to create algorithms capable of dependable, real-world performance.

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.