Author Description

Machine Learning can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Machine Learning pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. Machine Learning uses two types of techniques: supervised learning techniques (predictive techniques), which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques, which finds hidden patterns or intrinsic structures in input data. In this book, supervised learning techniques related to regression will be developed. More specifically, we will go deeper into the Linear Model Regression, Neural Networks Regression, Stepwise Regression, Partial Least Square Regression PLS, LASSO regression, LARS LASSO regression, RIDGE Regression, Elastic Net Regression, Robust Regression and other supervised techniques based in Regression. Variety of examples are solved using the MATLAB software.