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Computational Approach to Statistical Learning. (Record no. 30095)

MARC details
000 -LEADER
fixed length control field a
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200530b xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781138046375
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31015195
Item number ARN
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Arnold, Taylor
245 ## - TITLE STATEMENT
Title Computational Approach to Statistical Learning.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc CRC Press
Date of publication, distribution, etc 2019
Place of publication, distribution, etc Milton
300 ## - PHYSICAL DESCRIPTION
Extent xiii, 377 p.
Other physical details ill.
Dimensions 24 cm.
365 ## - TRADE PRICE
Price amount 59.99
Price type code GBP
Unit of pricing 98.20
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references.<br/>
520 ## - SUMMARY, ETC.
Summary, etc A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning - Mathematics
Topical term or geographic name as entry element Mathematical statistics
Topical term or geographic name as entry element Estimation theory
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Kane, Michae
Personal name Lewis, Bryan W.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Total Renewals Full call number Barcode Date last seen Date last borrowed Koha item type
    Dewey Decimal Classification     DAU DAU 29/05/2020 2 2 006.31015195 ARN 032287 01/10/2024 18/09/2024 Books