P. G. Madhavan - Data Science for IoT Engineers

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A Systems Analytics Approach

P. G. Madhavan, Ph.D.

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P. G. Madhavan. Data Science for IoT Engineers.

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To my wife, Ann

This book is the third iteration of the book I originally published in 2016 as Systems Analytics. The title reflected a new development effort in the field of machine learning, grounded firmly in systems theory. My intention in writing the first edition was to bring mathematically trained graduates in engineering, physics, mathematics, and allied fields into data science.

The objective of this edition, Data Science for IoT Engineers, remains the same. Part I, where I develop machine learning (ML) algorithms from the background of engineering courses such as control theory, signal processing, etc. is largely unchanged. However, dynamical systems-based Part II now takes a more detailed Multi-Input-Multi-Output (MIMO) systems approach, and develops a new and important form of digital twins called causal digital twin. This topic is significant, because on the one hand, digital twin is the seat of ML & AI in IoT solutions, but more important, causality is a critical factor in enabling prescriptive analytics, which is the real promise of Internet of Things (IoT). An epilogue has been added that introduces a new theory of random fields; it is shown that a new second-order property might have significant practical applications, and some of these are discussed.

In this part, we bring together machine learning, systems theory, linear algebra and digital signal processing. The intention is to make clear the similarity of basic theory and algorithms among these disparate fields. Hands-on exposure to machine learning is provided. This part concludes with a complete description of modern machine learning and a new ontology grounded in probability theory.

With the realization that business solutions are not one and done and they require ongoing measurement, tracking and fine-tuning, we embed machine learning in a closed-loop, real-time systems framework adaptive machine learning This naturally leads to the formal development of state-space formulation, Bayesian estimation, and Kalman filter. We develop a universal nonlinear, time-varying, dynamical machine learning solution which can faithfully model all the essential complexities of real-life business problems, and show how to apply it.

Developing the systems theme into the framework of digital twins as the action center for IoT-related machine learning, we explore several types of digital twins: (1) display, (2) forward and (3) inverse. Inverse digital twin is an example of a powerful form of causal modeling that captures the dynamics or kinetics of machinery in industrial applications. In the epilogue, we introduce some future development possibilities for data science from a complexity point of view.