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Data Science for IoT Engineers : A Systems Analytics Approach
| Data Science for IoT Engineers : A Systems Analytics Approach |
| Autore | Madhavan P. G |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2021 |
| Descrizione fisica | 1 online resource (170 pages) |
| Disciplina | 006.312024004678 |
| Soggetto topico | COMPUTERS / Desktop Applications / Presentation Software |
| Soggetto non controllato |
IOT
MATLAB computer science data analytics engineering mathematics physics |
| ISBN |
1-68392-640-4
1-68392-641-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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 |
| Record Nr. | UNINA-9910795555703321 |
Madhavan P. G
|
||
| Bloomfield : , : Mercury Learning & Information, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data Science for IoT Engineers : A Systems Analytics Approach
| Data Science for IoT Engineers : A Systems Analytics Approach |
| Autore | Madhavan P. G |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2021 |
| Descrizione fisica | 1 online resource (170 pages) |
| Disciplina | 006.312024004678 |
| Soggetto topico | COMPUTERS / Desktop Applications / Presentation Software |
| Soggetto non controllato |
IOT
MATLAB computer science data analytics engineering mathematics physics |
| ISBN |
1-68392-640-4
1-68392-641-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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 |
| Record Nr. | UNINA-9910810050903321 |
|
Madhavan P. G
|
||
| Bloomfield : , : Mercury Learning & Information, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||