CHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics
Chapter in the book
Publisher:Mercury Learning and Information
Paid access
|Dec 2021
Table of contents
Frontmatter
Contents
Preface
About the Author
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
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
15 chapters available
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PDF ISBN: 978-1-68392-641-2 | E-Pub ISBN: 978-1-68392-640-5 | Paperback ISBN: 978-1-68392-642-9 | DOI: 10.1515/9781683926412
Publisher: Mercury Learning and Information
Copyright owner: © 2021 Walter de Gruyter GmbH, Berlin/Boston
Publication date: 2021
Language: English
Pages: 158
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