Machine Learning (Record no. 2560)
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| 000 -LEADER | |
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| fixed length control field | 02902nam a22001697a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250603152938.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250603b |||||||| |||| 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 | , Kevin P. Murphy |
| 245 ## - TITLE STATEMENT | |
| Title | Machine Learning |
| Remainder of title | A Probabilistic Perspective |
| Statement of responsibility, etc. | , Kevin P. Murphy |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | London, England: |
| Name of publisher, distributor, etc. | MIT Press |
| Date of publication, distribution, etc. | 2012. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Page number | xxix+1071p |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Title | 1 Introduction – 2 Probability – 3 Generative models for discrete data – 4 Gaussian models – 5 Bayesian statistics – 6 Frequentist statistics – 7 Linear regression – 8 Logistic regression – 9 Generalized linear models and the exponential family – 10 Directed graphical models (Bayes nets) – 11 Mixture models and the EM algorithm – 12 Latent linear models – 13 Sparse linear models – 14 Kernels – 15 Gaussian processes – 16 Adaptive basis function models – 17 Markov and hidden Markov models – 18 State space models – 19 Undirected graphical models (Markov random fields) – 20 Exact inference for graphical models – 21 Variational inference – 22 More variational inference – 23 Monte Carlo inference – 24 Markov chain Monte Carlo (MCMC) inference – 25 Clustering – 26 Graphical model structure learning – 27 Latent variable models for discrete data – 28 Deep Learning – Notation – Bibliography – Indexes. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | "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."--Provided by publisher. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Currency | Koha item type |
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| Dewey Decimal Classification | Non-fiction | IIITDM Kurnool | IIITDM Kurnool | COMPUTER SCIENCE ENGINEERING | 03.06.2025 | New India Book Agency | 125.00 | 6183 DT 22/05/2025 | 006.31 MUR | 0007434 | 03.06.2025 | 125.00 | 03.06.2025 | USD | Books | |||||
| Dewey Decimal Classification | Non-fiction | IIITDM Kurnool | IIITDM Kurnool | COMPUTER SCIENCE ENGINEERING | 03.06.2025 | New India Book Agency | 125.00 | 6183 DT 22/05/2025 | 006.31 MUR | 0007435 | 03.06.2025 | 125.00 | 03.06.2025 | USD | Books |