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Machine learners : = archaeology of a data practice /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learners :/ Adrian Mackenzie.
Reminder of title:	archaeology of a data practice /
Author:	Mackenzie, Adrian,
Description:	1 PDF (xvi, 252 pages) :illustrations. :
Subject:	Information theory. -
Online resource:	http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8269017
ISBN:	9780262342551

Machine learners : = archaeology of a data practice /
Mackenzie, Adrian,1962-

Machine learners :archaeology of a data practice /Adrian Mackenzie. - 1 PDF (xvi, 252 pages) :illustrations.

Includes bibliographical references (pages 223-241) and index.

Introduction : into the data -- Diagramming machines -- Vectorization and its consequences -- Machines finding functions -- N=[upside down A]X : probabilization and the taming of machines -- Patterns and differences -- Regularizing and materializing objects -- Propagating subject positions -- Conclusion : out of the data.

Restricted to subscribers or individual electronic text purchasers.

Machine learning - programming computers to learn from data - has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking.Mackenzie focuses on machine learners -- either humans and machines or human-machine relations -- situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms -- writing code and writing about code -- and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. -- Provided by publisher.

Mode of access: World Wide Web

ISBN: 9780262342551Subjects--Topical Terms:

595305
Information theory.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: Q360 / .M3134 2017eb

Dewey Class. No.: 003/.54

Machine learners : = archaeology of a data practice /
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020 $a 9780262342551 $q electronic bk.
020 $z 0262342553 $q electronic bk.
020 $z 9780262036825 $q hardcover
020 $z 0262036827 $q hardcover
035 $a (CaBNVSL)mat08269017
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035 $a 8269017
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050 4 $a Q360 $b .M3134 2017eb
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100 1 $a Mackenzie, Adrian, $d 1962- $e author. $3 1213418
245 1 0 $a Machine learners : $b archaeology of a data practice / $c Adrian Mackenzie.
264 1 $a Cambridge, Massachusetts : $b The MIT Press, $c [2017]
264 2 $a [Piscataqay, New Jersey] : $b IEEE Xplore, $c [2017]
300 $a 1 PDF (xvi, 252 pages) : $b illustrations.
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
504 $a Includes bibliographical references (pages 223-241) and index.
505 0 $a Introduction : into the data -- Diagramming machines -- Vectorization and its consequences -- Machines finding functions -- N=[upside down A]X : probabilization and the taming of machines -- Patterns and differences -- Regularizing and materializing objects -- Propagating subject positions -- Conclusion : out of the data.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 $a Machine learning - programming computers to learn from data - has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking.Mackenzie focuses on machine learners -- either humans and machines or human-machine relations -- situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms -- writing code and writing about code -- and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. -- Provided by publisher.
530 $a Also available in print.
538 $a Mode of access: World Wide Web
588 $a Description based on print version record.
650 0 $a Information theory. $3 595305
650 0 $a Machine learning $x Philosophy. $3 1213419
650 0 $a Electronic data processing $x Philosophy. $3 1213420
655 4 $a Electronic books. $2 local $3 554714
710 2 $a IEEE Xplore (Online Service), $e distributor. $3 1213285
710 2 $a MIT Press, $e publisher. $3 1213286
856 4 2 $3 Abstract with links to resource $u http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8269017