  This article provides an overview of Supervised Machine Learning (SML) with a
focus on applications to banking. The SML techniques covered include Bagging
(Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural
Networks (NNs). We begin with an introduction to ML tasks and techniques. This
is followed by a description of: i) tree-based ensemble algorithms including
Bagging with RF and Boosting with GBMs, ii) Feedforward NNs, iii) a discussion
of hyper-parameter optimization techniques, and iv) machine learning
interpretability. The paper concludes with a comparison of the features of
different ML algorithms. Examples taken from credit risk modeling in banking
are used throughout the paper to illustrate the techniques and interpret the
results of the algorithms.
