| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
191121b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780262018029 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
MUR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Murphy, Kevin P. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning : a probabilistic perspective |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Cambridge |
| Name of publisher, distributor, etc |
MIT Press |
| Date of publication, distribution, etc |
2012 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxix, 1067 p. |
| Other physical details |
ill. |
| Dimensions |
23 cm. |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
110.00 |
| Price type code |
USD |
| Unit of pricing |
74.60 |
| 490 ## - SERIES STATEMENT |
|---|
| Series statement |
Adaptive computation and machine learning |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index.
|
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine-learning |
|
|---|
| Topical term or geographic name as entry element |
Artificial Intelligence and Semantics |
|
|---|
| Topical term or geographic name as entry element |
Business Intelligence Tools |
|
|---|
| Topical term or geographic name as entry element |
Enterprise Applications |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |