Stochastic Optimization and Adaptive Gradient Methods

This lecture covers stochastic optimization and adaptive gradient methods. It discusses the application of these methods in recommender systems and matrix factorization, focusing on user-item rating matrices. The lecture explains the stochastic gradient algorithm and its implementation for predicting missing ratings in the matrix. It also explores the Polyak-Ruppert averaging technique to smooth iterates in the optimization process. Various concepts such as bias terms, feature vectors, and matrix factorization are detailed with examples and theoretical derivations.