Machine Learning:The art and science of algorithm that make sense of Data (Record no. 2485)
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| 000 -LEADER | |
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| fixed length control field | 02275nam a22002057a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200224120458.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200118b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 978-1-316-50611-0 |
| 028 ## - PUBLISHER NUMBER | |
| Source | Allied Informatics, Jaipur |
| Bill Number | 7084 |
| Bill Date | 13/01/2020 |
| Purchase Year | 2019-20 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | BSDU |
| Language of cataloging | English |
| Transcribing agency | BSDU |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | FLA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Flach,Peter |
| 245 ## - TITLE STATEMENT | |
| Title | Machine Learning:The art and science of algorithm that make sense of Data |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | Cambridge University Press |
| Date of publication, distribution, etc. | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 396 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook. Prologue and Chapter 1 are freely available online Pedagogic features include boxes summarising relevant background material and a list of 'important points to remember' Epilogue includes open problems in machine learning Contents Prologue: a machine learning sampler 1. The ingredients of machine learning 2. Binary classification and related tasks 3. Beyond binary classification 4. Concept learning 5. Tree models 6. Rule models 7. Linear models 8. Distance-based models 9. Probabilistic models 10. Features 11. In brief: model ensembles 12. In brief: machine learning experiments Epilogue: where to go from here Important points to remember Bibliography Index. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine Learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Permanent Location | Current Location | Date acquired | Cost, normal purchase price | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Not For Loan | Reference | BSDU Knowledge Resource Center, Jaipur | BSDU Knowledge Resource Center, Jaipur | 2020-01-18 | 1595.00 | 006.31 FLA | 018040 | 2020-02-12 | 1595.00 | 2020-01-18 | Books |