This course will introduce the audience to some of the essential ingredients of learning in games under uncertainty (random matrix games), particularly reinforcement learning, cost-of-learning, Q-learning, mean-field learning, combined learning, heterogeneous learning and hybrid learning. The course will emphasize a stochastic approximation approach and will present one of the major challenges in the design of large-scale systems: the need for fully distributed learning algorithm schemes that consume a minimal amount of resources with a minimal amount of information exchange and yet with a very fast convergence time.