ARTIFICIAL INTELLIGENCE TECHNIQUES: SVM, DISCRIMINANT ANALYSIS AND DECISION TREES. 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 classification techniques including Support Vector Machine, Discriminant Analysis, Decision Trees, Regression Trees and Classification Trees. The approach is eminently practical, mixing methodological notes and examples and exercises solved with MATLAB software.