AdaBoost and Support Vector Machines (SVM) are two classification algorithms that have been revolutionizing the technology of pattern recognition since the late nineties. Both algorithms require considerably less "engineering" and parameter tuning than Multilayer Neural Networks, their predecessor that ruled applied pattern recognition in the late eighties and early nineties. The main objective of this talk is to describe AdaBoost and some of its variants from a practitioner's point of view. First, I will outline the main intuitions behind the algorithm, provide the algorithmic details, and give a short demonstration on a two-dimensional dataset to illustrate the main ideas. Then I will show some of the most important extensions and variants of the basic algorithm. Finally, I will discuss the design of the expert pool through some applications in the areas of image and language processing.