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Machine Learning Probabilistic Perspective by Kevin Murphy (35 results)
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Hardcover. Condition: Very Good. No Jacket. xxx,1067. 1950 grams. No jacket as issued. Light to mild ordinary shelfwear to the boards and edges. Dust at the top edge. Pages are crisp and entirely unmarked. It weighs almost 2 kilos. If you order from outside the United States, we will almost certainly request an additional payment to help cover the postage. Please feel welcome to message us with any questions, or a request for photographs. Every order includes tracking and is wrapped and robustly packaged with care in Tucson, AZ. ~Mesquite Booksellers.
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hardcover. Condition: Acceptable. DAMAGED - Binding is broken and loose.
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Hardcover. Condition: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority!
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Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Language: English
Published by The MIT Press (edition Illustrated), 2012
ISBN 10: 0262018020 ISBN 13: 9780262018029
Hardcover. Condition: Very Good. Illustrated. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Seller: Goodwill of Greater Milwaukee and Chicago, Racine, WI, U.S.A.
Seller rating 5 out of 5 stars
Condition: acceptable. Book is considered to be in acceptable condition. The actual cover image may not match the stock photo. Book may have one or more of the following defects: noticeable wear on the cover dust jacket or spine; curved, dog eared or creased page s ; writing or highlighting inside or on the edges; sticker s or other adhesive on cover; CD DVD may not be included; and book may be a former library copy.
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hardcover. Condition: Good. Illustrated. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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Hardcover. Condition: Near Fine. Brand New and unread with the tinniest wear on the lower bumper corners(hardly visible), pages clean and crisp, covers clean, no highlighting or underlining. 100% Satisfaction guaranteed or your money back. Thank you for your interest. Free tracking number for each order. We ship the same day or the next business day. geo1.
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Condition: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,2050grams, ISBN:9780262018029.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
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Condition: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
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Condition: New.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
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Condition: Gut. 1096 S. Zustand gut. Kleine Schrammen auf dem Einband. Schnitt leicht gewellt. 364 Sprache: Englisch Gewicht in Gramm: 1900 Gebundene Ausgabe, Maße: 21.11 cm x 4.09 cm x 23.65 cm.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Machine Learning : A Probabilistic Perspective
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
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Machine Learning : A Probabilistic Perspective
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Hardcover. Condition: new. New Copy. Customer Service Guaranteed.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: As New. Unread book in perfect condition.
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hardcover. Condition: New.
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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Machine Learning : A Probabilistic Perspective
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Hardcover. Condition: Brand New. 1st edition. 1104 pages. 9.10x8.10x1.70 inches. In Stock.
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Hardcover. Condition: New. ISBN:9780262018029.
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Hardcover. Condition: Sehr gut. Gebraucht - Sehr gut Sg - leichte Beschädigungen, Verschmutzungen, ungelesenes Mängelexemplar, gestempelt - A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package PMTK (probabilistic modeling toolkit) that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
