Introduction to Machine Learning (Record no. 59215)

MARC details
000 -LEADER
fixed length control field 02207nam a22002657a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220512160032.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220512b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9788120350786
028 ## - DISTRIBUTOR NAME
Distributor Name :Donated from Prof Madan Gopal
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 ALP
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Alpaydin, Ethem
245 ## - TITLE STATEMENT
Title Introduction to Machine Learning
Statement of responsibility, etc / Ethem Alpaydin
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Cambridge, Massachusetts
Name of publisher, distributor, etc : The MIT Press,
Date of publication, distribution, etc ©2014
300 ## - PHYSICAL DESCRIPTION
Extent xxii, 613 pages
Dimensions 24cm.
365 ## - TRADE PRICE
Price amount Donated
505 ## - FORMATTED CONTENTS NOTE
Contents note Introduction --<br/>Supervised learning --<br/>Bayesian decision theory --<br/>Parametric methods --<br/>Multivariate methods --<br/>Dimensionality reduction --<br/>Clustering --<br/>Nonparametric methods --<br/>Decision trees --<br/>Linear discrimination --<br/>Multilayer perceptrons --<br/>Local models --<br/>Kernel machines --<br/>Graphical models --<br/>Brief contents --<br/>Hidden markov models --<br/>Bayesian estimation --<br/>Combining multiple learners --<br/>Reinforcement learning --<br/>Design and analysis of machine learning experiments.
520 ## - SUMMARY, ETC.
Summary, etc Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of this title reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. --
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science,
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Special computer methods (e.g. AI, multimedia, VR)[4]
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Apprentissage automatique.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computers - General.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Alpaydin, Ethem
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Mathematics Departmental Library
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Full call number Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Mathematics Departmental Library Mathematics Departmental Library 12/05/2022 Donated from Prof Madan Gopal 006.31 ALP M375 12/05/2022 12/05/2022 Mathematics Departmental Library
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