ARTIFICIAL INTELLIGENCE TECHNIQUES: REGRESSION, SUPPORT VECTOR MACHINE AND NEURAL NETWORKS. Examples with MATLAB

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: predictive techniques (supervised learnig techniques) , which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised learning techniques), which finds hidden patterns or intrinsic structures in input data. The aim of predictive techniques is to build a model that makes predictions based on evidence in the presence of uncertainty. A predictive 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. Predictive techniques uses classification and regression techniques to develop predictive models. This book develops predictive regression techniques including Linear Models, Generalized Linear Models, Support Vector Machine Regression, Gaussian Proccess Regression, Ensemble Methods, Regression Trees, Regession Models with Neural Networks and Time Series Models with Neural Networks..