Machine Learning for Engineers (Record no. 814520)

MARC details
000 -LEADER
fixed length control field 01888nam a22002417a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240104s2023 |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781316512821
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
ISSN-L 9781316512821
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 620.00285
Item number SIM
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Simeone, Osvaldo
9 (RLIN) 879334
Relator term author
245 ## - TITLE STATEMENT
Title Machine Learning for Engineers
250 ## - EDITION STATEMENT
Edition statement 1st
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cambridge :
Name of publisher, distributor, etc. Cambridge University Press,
Date of publication, distribution, etc. c2023
300 ## - PHYSICAL DESCRIPTION
Extent xxii, 578 p.
Other physical details : ill
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes Bibliographical References and Index
520 ## - SUMMARY, ETC.
Summary, etc. SUMMARY<br/>This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 845
Topical term or geographic name entry element Machine Learning
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 1280
Topical term or geographic name entry element Engineering Data Processing
856 ## - ELECTRONIC LOCATION AND ACCESS
Link text TOC
Uniform Resource Identifier <a href="https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9781316512821.pdf">https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9781316512821.pdf</a>
856 ## - ELECTRONIC LOCATION AND ACCESS
Link text WEB LINK
Uniform Resource Identifier <a href="https://www.book2look.com/book/9781316512821">https://www.book2look.com/book/9781316512821</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Physical Form Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Stock Type Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Budget Year Cost, replacement price Accession Date Koha item type
    Dewey Decimal Classification Text, Hardcover     Reference Section Reference Section Reference Section 01/01/2024 22 Purchased 20034.81   620.00285 SIM 98523 16/02/2024 2023-24 20034.80 01/01/2024 Reference Collection