Convergence of Gradient Descent

This lecture covers the convergence of gradient descent for strongly convex functions with Lipschitz gradient. It explains how the gradient descent scheme converges to the minimum point of a function with a linear rate. The lecture also discusses generative models with a Poisson prior and the relationship between Poisson regression and logistic regression. Furthermore, it explores overfitting in polynomial regression and the concept of regularization in machine learning, emphasizing the importance of penalizing high coefficients to prevent overfitting.