Generalized Linear Models

This lecture covers the concept of Generalized Linear Models, including the assumptions, statistical properties, and practical applications. It delves into various topics such as high-dimensional statistics, Bayesian methods, compressed sensing, and perception. The instructor discusses the challenges of working with noisy data and the importance of understanding the linearized models in different contexts.