Statistical regression and classification: from linear models to machine learning (Record no. 208015)

000 -LEADER
fixed length control field 02503cam a22001938i 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 170313s2017 flu b 000 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781498710916
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.536
Item number M2S8
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Matloff, Norman
9 (RLIN) 354646
245 10 - TITLE STATEMENT
Title Statistical regression and classification: from linear models to machine learning
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Boca Raton
Name of publisher, distributor, etc CRC Press
Date of publication, distribution, etc 2017
300 ## - PHYSICAL DESCRIPTION
Extent xxxviii, 489 p.
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Texts in statistical science
9 (RLIN) 355654
520 ## - SUMMARY, ETC.
Summary, etc Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression:<br/>* A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods.<br/>* Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case.<br/>* In view of the voluminous nature of many modern datasets, there is a chapter on Big Data.<br/>* Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems.<br/>* Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics.<br/>* More than 75 examples using real data.<br/>The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis.<br/><br/><br/>https://www.crcpress.com/Statistical-Regression-and-Classification-From-Linear-Models-to-Machine/Matloff/p/book/9781498710916
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Regression analysis
9 (RLIN) 354647
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Vector analysis
9 (RLIN) 354648
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Regression - Statistics
9 (RLIN) 355655
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Item location Total Checkouts Total Renewals Full call number Barcode Date last seen Date last borrowed Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library General Stacks 22/01/2018 12 3844.08 Slot 1427 (0 Floor, East Wing) 1 1 519.536 M2S8 195793 08/09/2018 21/01/2018 4805.11 Books

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