MACHINE LEARNING WITH MATLAB. kNN, CLUSTER ANALYSIS AND PATTERN RECOGNITION WITH NEURAL NETWORKS

Machine learning can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Machine learning pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis like kNN (k-nearest neighbors algorithm), cluster analysis and pattern recognition across neural networks. Machine learning uses two types of techniques: Supervised Learning techniques (predictive techniques), which trains a model on known input and output data so that it can predict future outputs, and Supervised Learning techniques (descriptive techniques), which finds hidden patterns or intrinsic structures in input data. Unsupervised learning techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common descriptive 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. This book develops classification unsupervised learning techniques across neural networks.