Machine learning : (Record no. 16325)

MARC details
000 -LEADER
fixed length control field 01984nam a2200229Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field NULRC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250520102827.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250520s9999 xx 000 0 und d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780128015223
040 ## - CATALOGING SOURCE
Transcribing agency NULRC
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q 325.5 .T44 2015
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Theoridis, Sergios.
Relator term author
245 #0 - TITLE STATEMENT
Title Machine learning :
Remainder of title a Bayesian and optimization perspective /
Statement of responsibility, etc. Sergios Theodoridis
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. [Place of publication not identifed] :
Name of publisher, distributor, etc. [publisher not identified],
Date of publication, distribution, etc. c2015
300 ## - PHYSICAL DESCRIPTION
Extent 1,075 pages :
Other physical details illustrations ;
Dimensions 24 cm.
365 ## - TRADE PRICE
Price amount USD180.91
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes index.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Chapter1. Probability and stochastic processes -- Chapter2. Learning in parametric modeling: basic concepts and directions -- Chapter3. Mean-square error linear estimation -- Chapter4. Stochastic gradient descent: the LMS algorithm -- Chapter5. The least-squares family -- Chapter6. Classification: a tour of the classics -- Chapter7. Parameter learning: a convex analytic path -- Chapter8. Sparsity-aware learning: concepts and theoretical foundations -- Chapter9. Sparcity-aware learning: algorithms and applications -- Chapter10. Learning in reproducing Kernel Hilbert spaces -- Chapter11. Bayesian learning: inference and the EM algorithm -- Chapter12. Bayesian learning: approximate inference and nonparametric models -- Chapter13. Monte Carlo methods -- Chapter14. Probabilistic graphical models: Part I -- Chapter15. Probabilistic graphical models: Part II -- Chapter16. Particle filtering -- Chapter17. Neural networks and deep learning -- Chapter18. Dimensionality reduction and Latent Variables Modeling .
520 ## - SUMMARY, ETC.
Summary, etc. This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches--which are based on optimization techniques--together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Library of Congress Classification     Gen. Ed - CEAS LRC - Graduate Studies National University - Manila General Circulation 12/04/2017 Purchased - Amazon 180.91   GC Q 325.5 .T44 2015 NULIB000014084 05/20/2025 c.1 05/20/2025 Books