Data science for IoT engineers: a systems analytics approach
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 conne...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Dulles, Virginia
Mercury Learning and Information
[2022]
|
| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | - 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 |
| Beschreibung: | xii, 156 Seiten Illustrationen, Diagramme 229 mm |
| ISBN: | 9781683926429 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV048218268 | ||
| 003 | DE-604 | ||
| 005 | 20220719 | ||
| 007 | t | ||
| 008 | 220513s2022 a||| |||| 00||| eng d | ||
| 020 | |a 9781683926429 |c pbk |9 978-1-68392-642-9 | ||
| 024 | 3 | |a 9781683926429 | |
| 035 | |a (OCoLC)1337117968 | ||
| 035 | |a (DE-599)BVBBV048218268 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29T | ||
| 100 | 1 | |a Madhavan, P.G. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Data science for IoT engineers |b a systems analytics approach |c P.G. Madhavan, Ph.D. |
| 264 | 1 | |a Dulles, Virginia |b Mercury Learning and Information |c [2022] | |
| 300 | |a xii, 156 Seiten |b Illustrationen, Diagramme |c 229 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a - 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a bicssc / Business communication & presentation | |
| 650 | 4 | |a bicssc / Economics | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033599043 | ||
Datensatz im Suchindex
| _version_ | 1804183997579788288 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Madhavan, P.G |
| author_facet | Madhavan, P.G |
| author_role | aut |
| author_sort | Madhavan, P.G |
| author_variant | p m pm |
| building | Verbundindex |
| bvnumber | BV048218268 |
| ctrlnum | (OCoLC)1337117968 (DE-599)BVBBV048218268 |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02691nam a2200313 c 4500</leader><controlfield tag="001">BV048218268</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220719 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">220513s2022 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781683926429</subfield><subfield code="c">pbk</subfield><subfield code="9">978-1-68392-642-9</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9781683926429</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1337117968</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048218268</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Madhavan, P.G.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data science for IoT engineers</subfield><subfield code="b">a systems analytics approach</subfield><subfield code="c">P.G. Madhavan, Ph.D.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Dulles, Virginia</subfield><subfield code="b">Mercury Learning and Information</subfield><subfield code="c">[2022]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xii, 156 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">229 mm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">- 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</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bicssc / Business communication & presentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bicssc / Economics</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033599043</subfield></datafield></record></collection> |
| id | DE-604.BV048218268 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:50:02Z |
| indexdate | 2024-07-10T09:32:20Z |
| institution | BVB |
| isbn | 9781683926429 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033599043 |
| oclc_num | 1337117968 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | xii, 156 Seiten Illustrationen, Diagramme 229 mm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Mercury Learning and Information |
| record_format | marc |
| spelling | Madhavan, P.G. Verfasser aut Data science for IoT engineers a systems analytics approach P.G. Madhavan, Ph.D. Dulles, Virginia Mercury Learning and Information [2022] xii, 156 Seiten Illustrationen, Diagramme 229 mm txt rdacontent n rdamedia nc rdacarrier - 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 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 bicssc / Business communication & presentation bicssc / Economics |
| spellingShingle | Madhavan, P.G Data science for IoT engineers a systems analytics approach bicssc / Business communication & presentation bicssc / Economics |
| title | Data science for IoT engineers a systems analytics approach |
| title_auth | Data science for IoT engineers a systems analytics approach |
| title_exact_search | Data science for IoT engineers a systems analytics approach |
| title_exact_search_txtP | Data science for IoT engineers a systems analytics approach |
| title_full | Data science for IoT engineers a systems analytics approach P.G. Madhavan, Ph.D. |
| title_fullStr | Data science for IoT engineers a systems analytics approach P.G. Madhavan, Ph.D. |
| title_full_unstemmed | Data science for IoT engineers a systems analytics approach P.G. Madhavan, Ph.D. |
| title_short | Data science for IoT engineers |
| title_sort | data science for iot engineers a systems analytics approach |
| title_sub | a systems analytics approach |
| topic | bicssc / Business communication & presentation bicssc / Economics |
| topic_facet | bicssc / Business communication & presentation bicssc / Economics |
| work_keys_str_mv | AT madhavanpg datascienceforiotengineersasystemsanalyticsapproach |