The sparse grid method is a special discretization technique, which allows to cope with the curse of dimensionality to some extent. It is based on a hierarchical basis and a sparse tensor product decompositon. Sparse grids have been successfully used to solve partial differential equations in the past and, more recently, have been shown to be competitive for learning problems as well. The lecture will provide a general introduction to the major properties of sparse grids and present the sparse grid combination technique for classification and regression.