MACHINE LEARNING WITH MATLAB. SUPERIVISED LEARNING AND REGRESSION

Artificial Intelligence combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models. Classification techniques predict categorical responses and Regression techniques predict continuous responses. This book develops Regression Techniques including Linear Regression, Generalized Linear Regression, Support Vector Machine Regression, Gaussian Procces Regression, Ensemble Methods (Boostting, Random Forest and Bagging), Regression Trees, Regression Models with Neural Networks and Time Series Models with Neural Networks