This book is designed to introduce the concepts of data science to professionals in engineering, physics, mathematics, and allied fields. It is a workbook with MATLAB code that creates a common framework and points out various interconnections related to industry. This will allow the reader to connect previous subject knowledge to data science, machine learning, or analytics and apply it to IoT applications. Part One brings together subjects in machine learning, systems theory, linear algebra, digital signal processing, and probability theory. Part Two (Systems Analytics) develops a "universal" nonlinear, time-varying dynamical machine learning solution that can faithfully model all the essential complexities of real-life business problems and shows how to apply it.

Features
+Introduces the concepts of data science to professionals in engineering, physics, mathematics, and allied fields
+Develops a "universal," nonlinear, dynamical machine learning solution to model and apply the complexities of modern applications in IoT
+Covers topics such as machine learning, systems theory, linear algebra, digital signal processing, probability theory, state-space formulation, Bayesian estimation, Kalman filter, causality, and digital twins.

Table of Contents
Part One. 1: Machine Learning from Multiple Perspectives. 2: Introduction to Machine Learning. 3: Systems Theory, Linear Algebra, and Analytics Basics. 4: Modern Machine Learning. Part Two. Systems Analytics. 5: Systems Theory Foundations of Machine Learning. 6: State Space Model and Bayes Filter. 7: The Kalman Filter for Adaptive Machine Learning. 8: The Need for Dynamical Machine Learning. 9: Digital Twins. Epilogue: A New Random Field Theory. Index.

About the Author
P. G. Madhavan, PhD has an extensive background in the Internet of Things (IoT), machine learning, digital twins, and wireless technologies in roles such as Chief IoT Officer and IoT Product Manager at large corporations (including Rockwell Automation, GE Aviation, and NEC).