The subject of this chapter is Ensemble Learning in which the system is characterized by an ensemble of M models. We shall concentrate on supervised classification using a Multiple Classifier System (MCS). We start by introducing several techniques for creating a diverse set of classifiers, including bagging, boosting, subspace techniques and switching class labels. We then discuss different methods for combining the multiple classifiers including BKS (Behavior-Knowledge Systems), Pairwise Fusion Matrix, Borda Count, Boosting, Stacking, Mixture of Experts and Ensembles of Convolutional Neural Networks.