Machine learning fundamentals : a concise introduction (Record no. 200818)
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| fixed length control field | 02022nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260714112035.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260714b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 978-1108940023 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1108940021 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 JIA-M |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Jiang, Hui. |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning fundamentals : a concise introduction |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Cambridge ; |
| Name of publisher | Cambridge University Press, |
| Year of publication | 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xviii, 373p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Machine Learning Fundamentals: A Concise Introduction provides an accessible introduction to the principles and techniques of modern machine learning. The book explains the mathematical foundations of supervised and unsupervised learning, probabilistic models, optimization methods, neural networks, deep learning, and statistical learning theory. Practical examples and concise explanations make it suitable for undergraduate and graduate students as well as researchers seeking an introduction to machine learning. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books and Monographs |
| Full call number | Accession Number | Koha item type | Lost status | Damaged status | Permanent Location | Current Location | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price |
|---|---|---|---|---|---|---|---|---|---|---|
| 006.31 JIA-M | 103078 | Books and Monographs | Central Library, NIT Jalandhar | Central Library, NIT Jalandhar | General Stacks | 13.07.2026 | Delhi, Narendra Publishing House | 5726.70 |