  Boosted decision trees are a very powerful machine learning technique. After
introducing specific concepts of machine learning in the high-energy physics
context and describing ways to quantify the performance and training quality of
classifiers, decision trees are described. Some of their shortcomings are then
mitigated with ensemble learning, using boosting algorithms, in particular
AdaBoost and gradient boosting. Examples from high-energy physics and software
used are also presented.
