Many problems in the real life can be modelled as classification problems: the early detection of diseases or the granting of credit to a certain individual, among others. Supervised Classification handles this issue by learning from a sample, with the aim of being able to infer the class of forthcoming observations. Random Forests  is one of the most popular Supervised Classification techniques; it is but a collection of Classification Trees  on which randomness is applied somehow. Along this talk, we will mainly focus on how Classification Trees are built. Last, future research in this area will be discussed.
 Leo Breiman. “Random forests.” Machine Learning, 45(1):5–32, 2001.
 Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen. “Classification and regression trees.” CRC press, 1984.
 Trabajo Fin de Máster. Universidad de Sevilla. Máster Universitario en Matemáticas. María Cristina Molero del Río. “Aprendizaje supervisado mediante Random Forests.” https://idus.us.es/xmlui/handle/11441/63236