The first part of the lecture centers on the qualitative difference between small-scale and large-scale learning problem. Whereas small-scale learning problems are subject to the usual approximation - estimation tradeoff, large-scale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithms in non-trivial ways. Unlikely optimization algorithm such as stochastic gradient show amazing performance for large-scale machine learning problems. The second part makes a detailed overview of stochastic learning algorithms applied to both linear and nonlinear models. In particular I would like to spend time on the use of stochastic gradient for structured learning problems and on the subtle connection between nonconvex stochastic gradient and active learning.