Machine Learning Safety / (Record no. 7157)

MARC details
000 -LEADER
fixed length control field 02037 a2200229 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250923124031.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241209b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789811968136
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Item number HuaM
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Huang, Xiaowei
245 ## - TITLE STATEMENT
Title Machine Learning Safety /
Statement of responsibility, etc Xiaowei Huang, Baojie Jin and Wenjie Ruan
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Singapore:
Name of publisher Springer;
Year of publication ©2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages xvii, 321p.
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Artificial Intelligence : Foundations, Theory, and Algorithms
520 ## - SUMMARY, ETC.
Summary, etc Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities.<br/>The book aims to improve readers’ awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Artificial Intelligence
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Machine Learning
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Jin, Baojie
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Ruan, Wenjie
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Collection code Home library Current library Shelving location Date acquired Source of acquisition Purchase Price Bill number Full call number Accession Number Copy number Print Price Bill Date/Price effective from Koha item type
      Computer Science Indian Institute of Technology Tirupati Indian Institute of Technology Tirupati General Stacks 30/07/2025 Today and Tomorrows Printers and Publishers 4938.00 TTPP/132/2025-26 006.3 HuaM (11517) 11517 Copy 1 7054.29 30/07/2025 Books