Optimization for Machine Learning: Faster Gradient Descent

This lecture covers the concept of faster gradient descent in optimization for machine learning, focusing on projected gradient descent. It discusses the possibility of exponential decrease in error, strongly convex functions, smooth and strongly convex functions, and the properties of projection. The lecture also explains the algorithm of projected gradient descent, its idea of projecting onto a set after each step, and the results for projected gradient descent over closed and convex sets. Additionally, it explores constrained optimization problems and the transformation of such problems into unconstrained ones.