Probabilistic machine learning: An introduction (Record no. 1320381)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01411nam a2200229 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250508094955.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250508b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780262046824 |
| 037 ## - SOURCE OF ACQUISITION | |
| Terms of availability | Textual |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | RTL |
| Transcribing agency | RTL |
| 084 ## - COLON CLASSIFICATION NUMBER | |
| Classification number | D6,9(B) R2 |
| Assigning agency | RTL |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Murphy, Kevin P. |
| 9 (RLIN) | 29210 |
| 245 ## - TITLE STATEMENT | |
| Title | Probabilistic machine learning: An introduction |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Cambridge |
| Name of publisher, distributor, etc. | The MIT Press |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxix, 826 p. ill. |
| Other physical details | Includes bibliographical references and index |
| 490 ## - SERIES STATEMENT | |
| Series statement | Adaptive computation and machine learning |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.<br/><br/>This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Probabilities |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Statistics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Colon Classification (CC) |
| Suppress in OPAC | No |
| Koha item type | Textbook |
| Classification part | D6,9(B) R2 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Colon Classification (CC) | Ratan Tata Library | Ratan Tata Library | 2025-05-08 | D6,9(B) R2 | RT1528398 | 2025-05-08 | 2025-05-08 | Textbook |