Data Science for IoT Engineers : : A Systems Analytics Approach / / P. G. Madhavan.
This book introduces 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 su...
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Place / Publishing House: | Dulles, VA : : Mercury Learning and Information, , [2021] ©2021 |
Year of Publication: | 2021 |
Language: | English |
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Physical Description: | 1 online resource (158 p.) |
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Madhavan, P. G., author. aut http://id.loc.gov/vocabulary/relators/aut Data Science for IoT Engineers : A Systems Analytics Approach / P. G. Madhavan. Dulles, VA : Mercury Learning and Information, [2021] ©2021 1 online resource (158 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Frontmatter -- Contents -- Preface -- About the Author -- PART I Machine Learning from Multiple Perspectives -- CHAPTER 1 Overview of Data Science -- CHAPTER 2 Introduction to Machine Learning -- CHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics -- CHAPTER 4 “Modern” Machine Learning -- PART II Systems Analytics -- CHAPTER 5 Systems Theory Foundations of Machine Learning -- CHAPTER 6 State Space Model and Bayes Filter -- CHAPTER 7 The Kalman Filter for Adaptive Machine Learning -- CHAPTER 8 The Need for Dynamical Machine Learning: The Bayesian Exact Recursive Estimation -- CHAPTER 9 Digital Twins -- Epilogue A New Random Field Theory -- Index restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star This book introduces 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:Develops a “universal,” nonlinear, dynamical machine learning solution to model and apply the complexities of modern applications in IoTCovers 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. Issued also in print. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 08. Aug 2023) Artificial Intelligence. Data. Systems Engineering. COMPUTERS / Desktop Applications / Presentation Software. bisacsh IOT. MATLAB. computer science. data analytics. engineering. mathematics. physics. EPUB 9781683926405 print 9781683926429 https://www.degruyter.com/isbn/9781683926412 Cover https://www.degruyter.com/document/cover/isbn/9781683926412/original |
language |
English |
format |
eBook |
author |
Madhavan, P. G., Madhavan, P. G., |
spellingShingle |
Madhavan, P. G., Madhavan, P. G., Data Science for IoT Engineers : A Systems Analytics Approach / Frontmatter -- Contents -- Preface -- About the Author -- PART I Machine Learning from Multiple Perspectives -- CHAPTER 1 Overview of Data Science -- CHAPTER 2 Introduction to Machine Learning -- CHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics -- CHAPTER 4 “Modern” Machine Learning -- PART II Systems Analytics -- CHAPTER 5 Systems Theory Foundations of Machine Learning -- CHAPTER 6 State Space Model and Bayes Filter -- CHAPTER 7 The Kalman Filter for Adaptive Machine Learning -- CHAPTER 8 The Need for Dynamical Machine Learning: The Bayesian Exact Recursive Estimation -- CHAPTER 9 Digital Twins -- Epilogue A New Random Field Theory -- Index |
author_facet |
Madhavan, P. G., Madhavan, P. G., |
author_variant |
p g m pg pgm p g m pg pgm |
author_role |
VerfasserIn VerfasserIn |
author_sort |
Madhavan, P. G., |
title |
Data Science for IoT Engineers : A Systems Analytics Approach / |
title_sub |
A Systems Analytics Approach / |
title_full |
Data Science for IoT Engineers : A Systems Analytics Approach / P. G. Madhavan. |
title_fullStr |
Data Science for IoT Engineers : A Systems Analytics Approach / P. G. Madhavan. |
title_full_unstemmed |
Data Science for IoT Engineers : A Systems Analytics Approach / P. G. Madhavan. |
title_auth |
Data Science for IoT Engineers : A Systems Analytics Approach / |
title_alt |
Frontmatter -- Contents -- Preface -- About the Author -- PART I Machine Learning from Multiple Perspectives -- CHAPTER 1 Overview of Data Science -- CHAPTER 2 Introduction to Machine Learning -- CHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics -- CHAPTER 4 “Modern” Machine Learning -- PART II Systems Analytics -- CHAPTER 5 Systems Theory Foundations of Machine Learning -- CHAPTER 6 State Space Model and Bayes Filter -- CHAPTER 7 The Kalman Filter for Adaptive Machine Learning -- CHAPTER 8 The Need for Dynamical Machine Learning: The Bayesian Exact Recursive Estimation -- CHAPTER 9 Digital Twins -- Epilogue A New Random Field Theory -- Index |
title_new |
Data Science for IoT Engineers : |
title_sort |
data science for iot engineers : a systems analytics approach / |
publisher |
Mercury Learning and Information, |
publishDate |
2021 |
physical |
1 online resource (158 p.) Issued also in print. |
contents |
Frontmatter -- Contents -- Preface -- About the Author -- PART I Machine Learning from Multiple Perspectives -- CHAPTER 1 Overview of Data Science -- CHAPTER 2 Introduction to Machine Learning -- CHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics -- CHAPTER 4 “Modern” Machine Learning -- PART II Systems Analytics -- CHAPTER 5 Systems Theory Foundations of Machine Learning -- CHAPTER 6 State Space Model and Bayes Filter -- CHAPTER 7 The Kalman Filter for Adaptive Machine Learning -- CHAPTER 8 The Need for Dynamical Machine Learning: The Bayesian Exact Recursive Estimation -- CHAPTER 9 Digital Twins -- Epilogue A New Random Field Theory -- Index |
isbn |
9781683926412 9781683926405 9781683926429 |
url |
https://www.degruyter.com/isbn/9781683926412 https://www.degruyter.com/document/cover/isbn/9781683926412/original |
illustrated |
Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
000 - Computer science, knowledge & systems |
dewey-ones |
006 - Special computer methods |
dewey-full |
006.312024004678 |
dewey-sort |
16.312024004678 |
dewey-raw |
006.312024004678 |
dewey-search |
006.312024004678 |
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Data Science for IoT Engineers : A Systems Analytics Approach / |
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