Lecture
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.