ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press] Softcover The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
[Goring-By-Sea, WS, United Kingdom] [Publication Year: 2012]

MIT Press , pp. 1104 . Hardback. New. MIT Press ISBN 0262018020 9780262018029 [US]

MIT Press 2012 HARDBACK New P 1104.

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press Ltd, Cambridge, Mass.] Hardcover Hardcover. 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 ...

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press Ltd Aug 2012] Hardcover Neuware - 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 ...
Show/Hide image

Hardback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. ISBN 0262018020 9780262018029 [GB]

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press] Hardcover New Book. Shipped from UK. Established seller since 2000.
[Fairford, GLOS, United Kingdom] [Publication Year: 2012]

MIT Press 9/18/2012 12: 00: 00 AM Hardcover PLEASE NOTE, WE DO NOT SHIP TO DENMARK. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the UK.

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press] Hardcover Used Book
[STAFFORD, TX, U.S.A.] [Publication Year: 2012]

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press Ltd] Hardcover First Edition 2012. 1st Edition. Hardcover. Series: Adaptive Computation and Machine Learning Series. Num Pages: 1104 pages, 300 color illus., 165 b&w illus. BIC Classification: UYQM. Category: (G) General (US: Trade). Dimension: 241 x 213 x 44. Weight in Grams: 1958. . . . . .
[Galway, GY, Ireland] [Publication Year: 2012]

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press] Hardcover Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.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 fo.
[Greven, Germany] [Publication Year: 2012]
Show/Hide image

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press] Hardcover Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.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 fo.
[Greven, Germany] [Publication Year: 2012]
Show/Hide image

The MIT Press, Date: 2012-08-24. Hardcover. Good. 1.8110 in x 9.1339 in x 8.1102 in. Ships FAST! May contain highlighting/underlining/notes/etc. May have used stickers on cover. Access codes and supplements are not guaranteed to be included with used books.SP24 2012. The MIT Press ISBN 0262018020 9780262018029 [US]

The MIT Press, Date: 2012-08-24. Hardcover. Good. Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLY-NO ACCESS CODE, NO CD, Ships with Tracking 2012. The MIT Press ISBN 0262018020 9780262018029 [US]

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press] Hardcover pp. 1104 465 Illus. (300 Col.)
[Hounslow, United Kingdom] [Publication Year: 2012]

The MIT Press 2012 Illustrated Hard cover Very good Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.

Mit Press 2012 Hard cover Good 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).

The MIT Press 2012-08-24 Hardcover Good Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLY-NO ACCESS CODE, NO CD, Ships with Tracking.

The MIT Press 2012-08-24 Hardcover Good USED-VARIOUS AMOUNTS OF WEAR-POSSIBLE HIGHLIGHTS-WE CANNOT GUARANTEE SUPPLEMENTS SUCH AS CD, ACCESS CODE, OR INFO TRAC.

Cambridge Mit Press 2012 Hard cover Good. Sewn binding. Cloth over boards. 1104 p. Contains: Illustrations, black & white, Illustrations, color. Adaptive Computation and Machine Learning. 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.

Cambridge Mit Press 2012 Hard cover New. Sewn binding. Cloth over boards. 1104 p. Contains: Illustrations, black & white, Illustrations, color. Adaptive Computation and Machine Learning.

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: Mit Pr] Hardcover 1st edition. 1104 pages. 9.10x8.10x1.70 inches. In Stock.
[Exeter, United Kingdom] [Publication Year: 2012]

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press] Hardcover DAMAGED - Acceptable Condition and Unread! Text is clean and unmarked! Bruise/tear/crease to cover. Has a small black line or red dot on bottom/exterior edge of pages.
[McKeesport, PA, U.S.A.] [Publication Year: 2012]

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press (edition Illustrated)] Hardcover Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported
[Philadelphia, PA, U.S.A.] [Publication Year: 2012]

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press] Hardcover
[Austin, TX, U.S.A.] [Publication Year: 2012]