Physics of data science and machine learning:
Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mech...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press Taylor & Francis
2022
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| Ausgabe: | First edition |
| Schlagworte: | |
| Zusammenfassung: | Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, whilst exploring neural networks and machine learning building on fundamental concepts of statistical and quantum mechanics.This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence.Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid the development of new and innovative machine learning and artificial intelligence tools.Key features:Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt.Free from endless derivations, instead equations are presented and explained strategically and explain why it is imperative to use them and how they will help in the task at hand.Illustrations and simple explanations help readers visualize and absorb the difficult to understand concepts.Ijaz A. Rauf is Adjunct Professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an Associate Researcher at Ryerson University, Toronto, Canada and President of the Eminent-Tech Corporation, Bradford, ON, Canada |
| Beschreibung: | Chapter 1: Introduction; Chapter 2: An Overview of Classical Mechanics; Chapter 3: An Overview of Quantum Mechanics; Chapter 4: Probabilistic Physics; Chapter 5: Design of Experiments and Analyses; Chapter 6: Basics of Machine Learning; Chapter 7: Prediction, Optimization, and New Knowledge Development |
| Beschreibung: | xvi, 194 Seiten Illustrationen, Diagramme |
| ISBN: | 9780367768584 9781032074016 |
Internformat
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| 520 | |a Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, whilst exploring neural networks and machine learning building on fundamental concepts of statistical and quantum mechanics.This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence.Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid the development of new and innovative machine learning and artificial intelligence tools.Key features:Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt.Free from endless derivations, instead equations are presented and explained strategically and explain why it is imperative to use them and how they will help in the task at hand.Illustrations and simple explanations help readers visualize and absorb the difficult to understand concepts.Ijaz A. Rauf is Adjunct Professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an Associate Researcher at Ryerson University, Toronto, Canada and President of the Eminent-Tech Corporation, Bradford, ON, Canada | ||
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Datensatz im Suchindex
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| discipline | Informatik |
| discipline_str_mv | Informatik |
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| id | DE-604.BV047505965 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:20:18Z |
| indexdate | 2024-07-10T09:13:57Z |
| institution | BVB |
| isbn | 9780367768584 9781032074016 |
| language | English |
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| owner_facet | DE-29T DE-20 |
| physical | xvi, 194 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | CRC Press Taylor & Francis |
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| spelling | Rauf, Ijaz A. Verfasser aut Physics of data science and machine learning Ijaz A. Rauf First edition Boca Raton ; London ; New York CRC Press Taylor & Francis 2022 xvi, 194 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Chapter 1: Introduction; Chapter 2: An Overview of Classical Mechanics; Chapter 3: An Overview of Quantum Mechanics; Chapter 4: Probabilistic Physics; Chapter 5: Design of Experiments and Analyses; Chapter 6: Basics of Machine Learning; Chapter 7: Prediction, Optimization, and New Knowledge Development Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, whilst exploring neural networks and machine learning building on fundamental concepts of statistical and quantum mechanics.This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence.Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid the development of new and innovative machine learning and artificial intelligence tools.Key features:Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt.Free from endless derivations, instead equations are presented and explained strategically and explain why it is imperative to use them and how they will help in the task at hand.Illustrations and simple explanations help readers visualize and absorb the difficult to understand concepts.Ijaz A. Rauf is Adjunct Professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an Associate Researcher at Ryerson University, Toronto, Canada and President of the Eminent-Tech Corporation, Bradford, ON, Canada bisacsh / COMPUTERS / Machine Theory bisacsh / SCIENCE / Physics Physik (DE-588)4045956-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Physik (DE-588)4045956-1 s Maschinelles Lernen (DE-588)4193754-5 s Datenanalyse (DE-588)4123037-1 s DE-604 Erscheint auch als Online-Ausgabe 978-1-003-20674-3 |
| spellingShingle | Rauf, Ijaz A. Physics of data science and machine learning bisacsh / COMPUTERS / Machine Theory bisacsh / SCIENCE / Physics Physik (DE-588)4045956-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4045956-1 (DE-588)4193754-5 (DE-588)4123037-1 |
| title | Physics of data science and machine learning |
| title_auth | Physics of data science and machine learning |
| title_exact_search | Physics of data science and machine learning |
| title_exact_search_txtP | Physics of data science and machine learning |
| title_full | Physics of data science and machine learning Ijaz A. Rauf |
| title_fullStr | Physics of data science and machine learning Ijaz A. Rauf |
| title_full_unstemmed | Physics of data science and machine learning Ijaz A. Rauf |
| title_short | Physics of data science and machine learning |
| title_sort | physics of data science and machine learning |
| topic | bisacsh / COMPUTERS / Machine Theory bisacsh / SCIENCE / Physics Physik (DE-588)4045956-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
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