Mathematics for the "Machine Learning" Applications

Mathematics for the "Machine Learning" ApplicationsMachine Learning is a motivation for people to learn mathematics. This book may be used as a guidebook to the mathematical literatures that forms the foundations of Machine Learning. We pointed out the usefulness of mathematical concepts in the context of Machine Learning problems. With representative examples of Machine Learning, we have focused on the mathematical concepts behind the models. This book is designed to be specific about the concepts

Machine Learning is a motivation for people to learn mathematics. This book may be used as a guidebook to the mathematical literatures that forms the foundations of Machine Learning. We pointed out the usefulness of mathematical concepts in the context of Machine Learning problems. With representative examples of Machine Learning, we have focused on the mathematical concepts behind the models. This book is designed to be specific about the concepts behind Machine Learning. We have pinched commentswherever necessary in the hope that they provide necessary guidance with respect to the big picture.The pervasiveness of Machine Learning allows users to not worry about the specifics of channels. Users can focus on extracting insights from data using off-the-shelf tools. This enables ordinary domain experts to benefit from machine learning. This book provides a foundation for thinking about the certification and risk management of Machine Learning systems. About the AuthorThe author (Subir Chakrabarty) is having multi-disciplined qualifications in engineering, operational research, management accountancy, & financial management and 48 years of experience in operation, plant revamping, project management, cost engineering, cost & financial management, and application of statistical packages (SPSS & SAS). His achievements include teaching, training, and grooming 4,500 post-graduates in MBA� post-graduate program.