000			03838nam a2200493 i 4500
001			8269017
003			IEEE
005			20190220121653.0
006			m o d
007			cr |n|||||||||
008			180227s2017 maua ob 001 eng d
020			_a9780262342551 _qelectronic bk.
020			_z0262342553 _qelectronic bk.
020			_z9780262036825 _qhardcover
020			_z0262036827 _qhardcover
035			_a(CaBNVSL)mat08269017
035			_a(IDAMS)0b00006486bffcc1
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ360 _b.M3134 2017eb
082	0	4	_a003/.54 _223
100	1		_aMackenzie, Adrian, _d1962- _eauthor.
245	1	0	_aMachine learners : _barchaeology of a data practice / _cAdrian Mackenzie.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c[2017]
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2017]
300			_a1 PDF (xvi, 252 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
504			_aIncludes bibliographical references (pages 223-241) and index.
505	0		_aIntroduction : 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			_aRestricted to subscribers or individual electronic text purchasers.
520			_aMachine 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			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on print version record.
650		0	_aInformation theory.
650		0	_aMachine learning _xPhilosophy.
650		0	_aElectronic data processing _xPhilosophy.
655		4	_aElectronic books.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aMIT Press, _epublisher.
776	0	8	_iPrint version: _aMackenzie, Adrian, 1962- _tMachine learners _z9780262036825 _w(DLC) 2017005343 _w(OCoLC)972093403
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8269017
999			_c39789 _d39789