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 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.Preface
About the Author
Machine Learning from Multiple Perspectives
Overview of Data Science
Canonical Business Problem
A Basic ML Solution
Systems Analytics
Digital Twins
References
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
Conclusion
Systems Theory, Linear Algebra, and Analytics Basics
Digital Signal Processing (DSP) Machine Learning (ML)
Linear Time Invariant (LTI) System
Linear Algebra
Conclusion
“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
Conclusion
Adaptive Machine Learning
What is Dynamics?
References
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
References
The Kalman Filter for Adaptive Machine Learning
Kernel Projection Kalman Filter
Optimized Operation of the KP-Kalman Filter
Reference
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
Conclusion
References
Epilogue
A New Random Field Theory
References
Index