| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240320b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780367458393 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
518.43 |
| Item number |
ARA |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Arangala, Crista |
| 245 ## - TITLE STATEMENT |
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| Title |
Linear algebra with machine learning and data |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
CRC Press, |
| Date of publication, distribution, etc |
2023 |
| Place of publication, distribution, etc |
Boca Raton : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xix, 289 p. ; |
| Other physical details |
ill., |
| Dimensions |
25 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
74.99 |
| Price type code |
£ |
| Unit of pricing |
109.40 |
| 490 ## - SERIES STATEMENT |
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| Series statement |
Textbooks in Mathematics Series |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data mining |
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| Topical term or geographic name as entry element |
Machine learning |
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| Topical term or geographic name as entry element |
Activation function |
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| Topical term or geographic name as entry element |
Adjacency matrix |
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| Topical term or geographic name as entry element |
Baum-Welch Algorithm |
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| Topical term or geographic name as entry element |
Census blocks |
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| Topical term or geographic name as entry element |
Chebyshev polynomials |
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| Topical term or geographic name as entry element |
Decision tree |
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| Topical term or geographic name as entry element |
Eigenvalues |
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| Topical term or geographic name as entry element |
Fiedler vector |
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| Topical term or geographic name as entry element |
Github link |
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| Topical term or geographic name as entry element |
Hidden Markov model |
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| Topical term or geographic name as entry element |
Laplacian matrix |
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| Topical term or geographic name as entry element |
Markov chain |
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| Topical term or geographic name as entry element |
Random matrices |
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| Topical term or geographic name as entry element |
Symmetric matrix |
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| Topical term or geographic name as entry element |
Vandermonde matrix |
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| Topical term or geographic name as entry element |
Viterbi Algorithm |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |