Machine learning : (Record no. 141894)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01902nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260804153202.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260804s2026 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780443292385 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | PK-LaUMT |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | THE-M |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Theodoridis, Sergios |
| 245 10 - TITLE STATEMENT | |
| Title | Machine learning : |
| Remainder of title | from the classics to deep networks, transformers, and diffusion models / |
| Statement of responsibility, etc | Sergios Theodoridis |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 3rd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | London : |
| Name of publisher, distributor, etc | Academic Press, |
| Date of publication, distribution, etc | 2026 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxx, 1190 p. |
| 500 ## - GENERAL NOTE | |
| General note | Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines. Bayesian learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs. |
| 546 ## - LANGUAGE NOTE | |
| Language note | Eng |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Online learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Home library | Current library | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|
| UMT Main Campus | UMT Main Campus | 2026-08-04 | 006.31 THE-M | 153866 | 2026-08-04 | 2026-08-04 | Books |