Machine Learning (Record no. 11917)
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fixed length control field | 02256nmm a2200181Ia 4500 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9780262018029 |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.31 |
Item number | M954M |
100 ## - MAIN ENTRY--PERSONAL NAME | |
Personal name | Murphy, Kevin P. |
Relator term | Author |
Language of a work | English |
9 (RLIN) | 745 |
245 #0 - TITLE STATEMENT | |
Title | Machine Learning |
Remainder of title | : A Probabilistic Perspective |
Statement of responsibility, etc. | / by Kevin P. Murphy. |
Medium | [Electronic Resource] |
260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
Place of publication, distribution, etc. | Cambridge, Mass |
Name of publisher, distributor, etc. | : The MIT Press, |
Date of publication, distribution, etc. | 2012 |
300 ## - PHYSICAL DESCRIPTION | |
Extent | 1071p. |
520 ## - SUMMARY, ETC. | |
Summary, etc. | 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. |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Machine Learning |
9 (RLIN) | 15461 |
Topical term or geographic name entry element | Probabilities |
9 (RLIN) | 15462 |
856 ## - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | <a href="http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=480968">http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=480968</a> |
Electronic format type | |
Link text | Click to Access the Online Book |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | e-Book |
Suppress in OPAC |
Withdrawn status | Lost status | Damaged status | Use restrictions | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type | Public note |
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e-Book For Access | Textbook | S. R. Ranganathan Learning Hub | S. R. Ranganathan Learning Hub | Online | 2022-09-20 | Infokart India Pvt. Ltd., New Delhi | 165.00 | 006.31 M954M | EB0053 | 2022-09-20 | 2022-09-20 | e-Book | Platform : EBSCO |