Generalization Error in Learning with Random Features

This lecture delves into the analysis of generalization error in learning theory, focusing on structured data and complex architectures. The instructor explains the hidden manifold model and the use of random features in high-dimensional regression and classification. The lecture presents a detailed derivation of the generalization error expression using the replica method from statistical physics, showcasing its application to various scenarios, including the intriguing double descent behavior observed in modern machine learning.