Madhavan P.G. Data Science for IoT Engineers. A Systems Analytics Approach

Mercury Learning and Information, 2022. — 171 p.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 the 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.FeaturesIntroduces 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.Machine Learning from Multiple Perspectives
Overview of Data Science.
Canonical Business Problem.
A Basic ML Solution.
Systems Analytics.
Digital Twins.Introduction to Machine Learning
Basic Machine Learning.
Normalization.
Data Exploration.
Parallel Coordinate Systems.
Feature Extraction.
Multiple Linear Regression.
Decision Tree.
Naïve Bayes.
Ensemble Method.
Unsupervised Learning.
K-Means Clustering.
Self-Organizing Map (SOM) Clustering.Systems Theory, Linear Algebra, and Analytics Basics
Digital Signal Processing (DSP) Machine Learning (ML).
Linear Time Invariant (LTI) System.
Linear Algebra.“Modern” Machine Learning
ML Formalism.
Bayes Generalization, the Hoeffding Inequality, and VC Dimension.
Formal Learning Methods.
Regularization & Recursive Least Squares.
Revisiting the Iris Problem.
Kernel Methods: Nonlinear Regression,
Bayesian Learning, and Kernel Regression.
Random Projection Machine Learning.
Random Projection Recursive Least Squares (RP-RLS).
ML Ontology.
Conditional Expectation and Big Data.
Big Data Estimation.Adaptive Machine Learning
What is Dynamics?Systems Analytics.
Systems Theory Foundations of.
Machine Learning.
Introduction-in-Stream Analytics.
Basics for Adaptive ML.
Exact Recursive Algorithms.
State Space Model and Bayes Filter
State-Space Model of Dynamical Systems.
Kalman Filter for the State-Space Model.
Special Combination of the Bayes Filter and Neural Networks.The Kalman Filter for Adaptive Machine Learning
Kernel Projection Kalman Filter.
Optimized Operation of the KP-Kalman Filter.The Need for Dynamical Machine Learning
The Bayesian Exact Recursive Estimation.
Need for Dynamical ML.
States for Decision-Making.
Summary of Kalman Filtering and Dynamical Machine Learning.
Digital Twins
Causality.
Inverse Digital Twin.
Inverse Model Framework.
Graph Causal Model.
Causality Insights.
Inverse Digital Twin Algorithm.
Simulation.Epilogue
A New Random Field Theory.
References.
Index