MARC details
| 000 -LEADER |
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| fixed length control field |
02361 a2200205 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230811b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781009218283 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
519.54 SAY |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Sayed, Ali H. |
| 245 ## - TITLE STATEMENT |
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| Title |
Inference and learning from data, Vol. 3: learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Cambridge, UK: |
| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
2023. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
li, 2165p.-3192p.: |
| Other physical details |
col. ill.; hbk.: |
| Dimensions |
25cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes Author Index and Subject Index |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.<br/><br/>Unique in its scale and depth, this is a comprehensive introduction to methods in data-driven learning and inference<br/>Over 1300 end-of-chapter problems (with solutions for instructors), 600 figures and 470 in-text solved examples across the three volumes<br/>A phenomenal contribution by a world authority in the field<br/>Covers sufficient topics across the volumes for the construction of a variety of courses covering a wide range of themes<br/><br/>https://www.cambridge.org/in/universitypress/subjects/engineering/communications-and-signal-processing/inference-and-learning-data-learning-volume-3?format=HB |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Inference |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Mathematical models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Statistical models |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
