Statistical learning (SL) is the third mainstream in machine learning research. The main goal of statistical learning theory is to provide a framework for studying problem of inference. That is of gaining knowledge, making predictions, making decisions or constructing models from a set of data. Statistical Learning provides an accessible overview of the field of statistical learning, an essential tool-set for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. Basic Overview of Statistical Learning Statistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. The field encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines. It refers to a vast set of tools for understanding data. These tools can be classified as supervised or unsupervised. Broadly speaking, supervised SL involves building a statistical model for predicting, or estimating, an output based on one or more inputs. Problems of this nature occur in fields as…