Machine and Deep Learning Algorithms and Applications

This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty and industry practitioners. The authors begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data.

Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications.

The book starts by introducing concepts in supervised, unsupervised and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. The authors then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, they cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.