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. Neural networks and their applications are a fundamental tool to develop work in Artificial Intelligence. The availability of large volumes of data and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics that includes Multivariate Data Analysis (MDA), Data Mining, Machine Learning and other Business Intelligence techniques. Data Mining an 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 techniques for classification and regression. Ensemble methods, boosting, bagging, random forest, decision trees and regression trees are included.
