Stochastic Gradient Descent Convergence Proof

This lecture covers the convergence proof of Stochastic Gradient Descent (SGD) with strongly-convex functions. It explains the steps to show convergence with respect to the optimal solution, focusing on the recursive relationship between the estimates. The instructor presents the derivation of the terms C and D, and discusses the choice of a to achieve a convergent bound. The lecture concludes by analyzing the algorithm's approach to the limiting bound and the impact of using a decreasing step-size on convergence properties.