Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the second part of a comprehensive lecture on generalization and optimization in machine learning, presented by Misha Belkin from the University of California, San Diego. Delve into the evolving understanding of generalization in machine learning, focusing on recent developments driven by empirical findings in neural networks. Examine how these discoveries have necessitated a reevaluation of theoretical foundations. Gain insights into the optimization process using gradient descent and discover why large non-convex systems are surprisingly easy to optimize through local methods. Enhance your knowledge of key concepts in data science and artificial intelligence through this in-depth presentation, part of IPAM's Mathematics of Intelligences Tutorials at UCLA.