DATA SCIENCE WITH MATLAB. UNSUPERVISED LEARNING TECHNIQUES: CLASSIFICATION

Data science is an interdisciplinary field that employs methods, algorithms, processes, and systems to extract insights and conclusions from structured and unstructured data. It combines elements of statistics, computer science, mathematics, and analytical techniques to solve problems, make predictions, and generate value from data. Machine learning techniques play a key role within the data analysis phase of data science. This book covers unsupervised learning techniques for classification such as hierarchical and non-hierarchical clustering techniques, cluster evaluation and visualization techniques, and pattern recognition. Additionally, the book explores the use of neural networks for cluster analysis and pattern recognition, including competitive neural networks, Self-Organizing Maps (SOMs), convolutional neural networks, autoencoders, and image classification. A variety of exercises are presented to facilitate the understanding of these techniques.