Overview

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. Unsupervised learning is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition.MATLAB provides tools to help you try out a variety of machine learning models and choose the best. Multivariate statistical techniques include supervised and unsupervised learning techniques. This book develops unsupervised learning techniques such as cluster analysis, dimension reduction, multidimensional scaling and feature selection.

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