Generalization in Learning with Random Features

This lecture delves into the concept of generalization in machine learning, focusing on the trade-off between underfitting and overfitting data. The instructor explains the teacher-student framework, worst-case and typical-case bounds, and the evaluation of generalization error. The lecture introduces the manifold model and discusses the relationship between generalization error and the number of samples in high-dimensional data models. It explores the use of random features and orthogonal projections in machine learning tasks, highlighting their impact on generalization error. The presentation also covers the double descent phenomenon, where the generalization error decreases after an interpolation threshold, and the importance of regularization in controlling model complexity.