Lecture
This lecture delves into the trade-off between complexity and risk in machine learning models, showcasing how test error decreases with increasing model complexity. It explores the benefits of overparametrization, the implicit bias of optimization algorithms, and the concept of implicit regularization. The discussion extends to the stability of optimization algorithms, the double descent phenomenon, and the probability of interpolation in high-dimensional datasets.