Machine learning : a first course for engineers and scientists (Record no. 32236)
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| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231101b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781108843607 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | LIN |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Lindholm, Andreas |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning : a first course for engineers and scientists |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Cambridge University Press, |
| Date of publication, distribution, etc | 2022 |
| Place of publication, distribution, etc | Cambridge : |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 338 p. ; |
| Other physical details | ill., |
| Dimensions | 27 cm. |
| 365 ## - TRADE PRICE | |
| Price amount | 54.99 |
| Price type code | GBP |
| Unit of pricing | 107.60 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | k-NN |
| Topical term or geographic name as entry element | Support vector machines |
| Topical term or geographic name as entry element | Deep neural networks |
| Topical term or geographic name as entry element | Gaussian processes |
| Topical term or geographic name as entry element | PCA |
| Topical term or geographic name as entry element | Adversarial networks |
| Topical term or geographic name as entry element | Logistic regression |
| Topical term or geographic name as entry element | Decision trees |
| Topical term or geographic name as entry element | K-means |
| Topical term or geographic name as entry element | Generative modeling |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Wahlström, Niklas |
| Personal name | Schön, Thomas B. |
| Personal name | Lindsten, Fredrik |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Date last borrowed | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | DAU | DAU | 01/11/2023 | 5916.92 | 1 | 006.31 LIN | 034450 | 12/09/2024 | 29/08/2024 | Books |