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Textbooks in Mathematics Ser.: Linear Algebra with Machine Learning and Data by Crista Arangala (2023, Hardcover)
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About this product
Product Identifiers
PublisherCRC Press LLC
ISBN-10036745839X
ISBN-139780367458393
eBay Product ID (ePID)13058359060
Product Key Features
Number of Pages365 Pages
Publication NameLinear Algebra with Machine Learning and Data
LanguageEnglish
Publication Year2023
SubjectAlgebra / Linear, General, Applied
TypeTextbook
Subject AreaMathematics
AuthorCrista Arangala
SeriesTextbooks in Mathematics Ser.
FormatHardcover
Dimensions
Item Length9.2 in
Item Width6.1 in
Additional Product Features
LCCN2022-047264
Dewey Edition23/eng20230203
IllustratedYes
Dewey Decimal518/.43
Table Of Content1 Graph Theory. 2. Stochastic Processes. 3. SVD and PCA. 4. Interpolation. 5. Optimization and Learning Techniques for Regression. 6. Decision Trees and Random Forests. 7. Random Matrices and Covariance Estimate. 8. Sample Solutions to Exercises.
SynopsisThis 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 and 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 are assumed; however, an overview of key concepts are presented in the Introduction and as needed throughout the text., 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., 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.
LC Classification NumberQA184.2.A73 2023
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