Optimization for Machine Learning: Accelerated Gradient Descent

This lecture covers Accelerated Gradient Descent, Gradient-free optimization, and their applications in machine learning. It discusses the speed of gradient descent on smooth convex functions, the concept of Nesterov's accelerated gradient descent, error bounds, potential functions, and the convergence rate for derivative-free random search. Additionally, it explores adaptive and other SGD methods such as Adagrad, Adam, and SignSGD, highlighting their advantages and practical implications.