Pattern Recognition and Machine Learning (Record no. 38039)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01754nam a22001697a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781493938438 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.64 BIS |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Christopher M Bishop |
| 245 ## - TITLE STATEMENT | |
| Title | Pattern Recognition and Machine Learning |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Springer |
| Place of publication | New York |
| Year of publication | 2009 |
| 500 ## - GENERAL NOTE | |
| General note | Shelf location-Costly Books |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | CSE |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Reference Book |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Damaged status | |
| Not for loan | |
| Withdrawn status | Collection code | Permanent Location | Current Location | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Full call number | Accession Number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Computer Science and Engineering | VAST Central Library | VAST Central Library | S-1/REF-COS | 18/11/2025 | Calicut Books | 7817.00 | 17740 | 006.64 BIS | 38132 | 21/11/2025 | Reference Book |