Linear algebra with machine learning and data
Material type:
TextSeries: Textbooks in mathematicsPublication details: Boca Raton : CRC Press, 2023. Description: xix, 289 pagesISBN: 9780367458393Subject(s): Algebras, Linear -- Textbooks | Machine learning -- Mathematics -- Textbooks | Data mining -- Mathematics -- TextbooksDDC classification: 512.64:004.85 Online resources: Table of content | Reviews Summary: This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application.
This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis. The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation.
Knowledge of mathematical techniques related to data analytics and exposure to interpretation of results within a data analytics context are particularly valuable for students studying undergraduate mathematics. Each chapter of this text takes the reader through several relevant case studies using real-world data.
All data sets, as well as Python and R syntax, are provided to the reader through links to Github documentation. Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics.
A basic knowledge of the concepts in a first Linear Algebra course is assumed; however, an overview of key concepts is presented in the Introduction and as needed throughout the text.
| Item type | Current library | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
Book
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NISER LIBRARY | 512.64:004.85 ARA-L (Browse shelf(Opens below)) | Available | 25650 |
Browsing NISER LIBRARY shelves Close shelf browser (Hides shelf browser)
| 512.64 STR-L Linear algebra and its applications | 512.64 VAL-L Linear algebra: an introduction to abstract mathematics | 512.64 ZHA-P Problems in linear algebra and matrix theory | 512.64:004.85 ARA-L Linear algebra with machine learning and data | 512.642 BAT-T Theory of finite linear spaces (the): combinatorics of points and lines | 512.642 BAT-T Theory of finite linear spaces (the): combinatorics of points and lines | 512.642 DAW-V Vectors and vector operators |
Includes bibliographical references and index.
This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application.
This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis. The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation.
Knowledge of mathematical techniques related to data analytics and exposure to interpretation of results within a data analytics context are particularly valuable for students studying undergraduate mathematics. Each chapter of this text takes the reader through several relevant case studies using real-world data.
All data sets, as well as Python and R syntax, are provided to the reader through links to Github documentation. Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics.
A basic knowledge of the concepts in a first Linear Algebra course is assumed; however, an overview of key concepts is presented in the Introduction and as needed throughout the text.
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