Data-driven approaches have swept all walks of science and engineering in recent years, with deep neural networks, deep reinforcement learning, and adversarial networks becoming the new staples that everyone uses to tackle a very wide variety of problems. While the empirical success of these methods is truly impressive when a lot of training data is available, there are still many problems that can in fact benefit from classical machine learning tools. In this talk, I will focus on showcasing the remarkable potential of latent factor analysis in the context of modern wireless communications. In particular, I will talk about edge-cell interferometry - a technique we recently devised that can reliably decode edge-cell users that are only 3dB above the noise floor, without requiring knowledge of their channels. I will also talk about how latent factor analysis can be used to tackle very hard estimation and optimization problems on the way to 5G and well beyond.