Madhavan P. G. :
P. G. Madhavan, Ph.D., 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).
No detailed deillegalscription available for "Data Science for IoT Engineers".
Designed to introduce the concepts of data science to professionals in engineering, physics, mathematics, and allied fields. This is a workbook with MATLAB code that creates a common framework and points out various interconnections related to industry.
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
Madhavan P. G. :
P. G. Madhavan, Ph.D., 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).
Über den Autor
P. G. Madhavan, Ph.D., 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).
Inhaltsverzeichnis
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
