MARC details
| 000 -LEADER |
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02941cam a22004817a 4500 |
| 001 - CONTROL NUMBER |
| control field |
23248697 |
| 003 - CONTROL NUMBER IDENTIFIER |
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
| control field |
20250306114240.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
| fixed length control field |
230728t20232023caua 001 0 eng d |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
| LC control number |
2023278332 |
| 015 ## - NATIONAL BIBLIOGRAPHY NUMBER |
| National bibliography number |
GBC307044 |
| Source |
bnb |
| 016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER |
| Record control number |
020868348 |
| Source |
Uk |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
| International Standard Book Number |
9789355422194 (pbk.) |
| 035 ## - SYSTEM CONTROL NUMBER |
| System control number |
(OCoLC)on1313074094 |
| 040 ## - CATALOGING SOURCE |
| Original cataloging agency |
YDX |
| Language of cataloging |
eng |
| Transcribing agency |
YDX |
| Modifying agency |
BDX |
| -- |
UKMGB |
| -- |
OCLCQ |
| -- |
GO9 |
| -- |
OCLCF |
| -- |
DLC |
| 042 ## - AUTHENTICATION CODE |
| Authentication code |
lccopycat |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
| Classification number |
Q325.5 |
| Item number |
.P78 2023 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
| Classification number |
006.31 |
| Edition number |
23 |
| Item number |
PRU.Y |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
| Personal name |
Pruksachatkun, Yada, |
| Relator term |
author. |
| 9 (RLIN) |
31806 |
| 245 10 - TITLE STATEMENT |
| Title |
Practicing trustworthy machine learning : |
| Remainder of title |
consistent, transparent, and fair AI pipelines / |
| Statement of responsibility, etc. |
by Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar. |
| 246 3# - VARYING FORM OF TITLE |
| Title proper/short title |
Consistent, transparent, and fair artifical intelligence pipelines |
| 250 ## - EDITION STATEMENT |
| Edition statement |
1st ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Place of production, publication, distribution, manufacture |
America; |
| -- |
Boston : |
| Name of producer, publisher, distributor, manufacturer |
O'Reilly, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Date of production, publication, distribution, manufacture, or copyright notice |
©2023 |
| 300 ## - PHYSICAL DESCRIPTION |
| Extent |
xxiv, 274 p. |
| Other physical details |
illustrations ; |
| Dimensions |
23 cm |
| 336 ## - CONTENT TYPE |
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
| Carrier type term |
volume |
| Source |
rdacarrier |
| 500 ## - GENERAL NOTE |
| General note |
Includes index. |
| 505 00 - FORMATTED CONTENTS NOTE |
| Title |
Preface -- |
| -- |
Privacy -- |
| -- |
Fairness and bias -- |
| -- |
Model explainability and interpretability -- |
| -- |
Robustness -- |
| -- |
Secure and trustworthy data generation -- |
| -- |
More state-of-the-art research questions -- |
| -- |
From theory to practice -- |
| -- |
An ecosystem of trust -- |
| -- |
Synthetic data generation tools -- |
| -- |
Other interpretability and explainability tool kits -- |
| -- |
Index -- |
| -- |
About the authors |
| 520 ## - SUMMARY, ETC. |
| Summary, etc. |
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. -- |
| Assigning source |
Provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Artificial intelligence |
| General subdivision |
Philosophy. |
| 9 (RLIN) |
31807 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Algorithms. |
| 9 (RLIN) |
31808 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Data mining. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Machine learning. |
| Source of heading or term |
fast |
| Authority record control number or standard number |
(OCoLC)fst01004795 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
| Personal name |
McAteer, Matthew, |
| Relator term |
author. |
| 9 (RLIN) |
31809 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
| Personal name |
Majumdar, Subhabrata, |
| Relator term |
author. |
| 9 (RLIN) |
31810 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
| Public note |
Supplemental information and resources |
| Uniform Resource Identifier |
<a href="https://github.com/matthew-mcateer/practicing_trustworthy_machine_learning">https://github.com/matthew-mcateer/practicing_trustworthy_machine_learning</a> |
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7 |
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cbc |
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copycat |
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2 |
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ncip |
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20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
| Source of classification or shelving scheme |
|
| Koha item type |
Books |
| Edition |
23 |
| Suppress in OPAC |
No |