Introduction to Machine Learning (Record no. 296515)

MARC details
000 -LEADER
fixed length control field 03289nam a22001697a 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9788120350786
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 ALP-I
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ethem Alpaydin
245 ## - TITLE STATEMENT
Title Introduction to Machine Learning
250 ## - EDITION STATEMENT
Edition statement 3rd ed [ eastern Economy Edition ]
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Patparganj, Delhi
Name of publisher, distributor, etc. PHI LearningPrivate limited
Date of publication, distribution, etc. 2018
300 ## - PHYSICAL DESCRIPTION
Extent xxii,613p.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Preface Notations 1. Introduction 2 Supervised Learning 3. Bayesian Decision Theory 4. Parametric Methods 5. Multivariate Methods 6. Dimensionality Reduction 7. Clustering 8. Nonparametric Methods 9. Decision Trees 10. Linear Discrimination 11. Multilayer Perceptrons 12. Local Models 13. Kernel Machines 14. Graphical Models 15. Hidden Markov Models 16. Bayesian Estimation 17. Combining Multiple Learners 18. Reinforcement Learning 19. Design and Analysis of Machine Learning Experiments Printed Pages: 635.
520 ## - SUMMARY, ETC.
Summary, etc. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semiparametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. This new edition of the book 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 perceptors 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 undergraduate and postgraduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. “Ethem Alpaydin`s Introduction to Machine Learning provides a nice blending of the topical coverage of machine learning (à la Tom Mitchell) with formal probabilistic foundations (à la Christopher Bishop). This newly updated version now introduces some of the most recent and important topics in machine learning (e.g., spectral methods, deep learning, and learning to rank) to students and researchers of this critically important and expanding field.” â€"John W. Sheppard, Professor of Computer Science, Montana State University “This volume is both a complete and accessible introduction to the machine learning world. This is a `Swiss Army knife` book for this rapidly evolving subject. Although intended as an introduction, it will be useful not only for students but for any professional looking for a comprehensive book in this field. Newcomers will find clearly explained concepts and experts will find a source for new references and ideas.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Date last checked out Price effective from Koha item type Checked out
        Dept. of Computational Biology and Bioinformatics Dept. of Computational Biology and Bioinformatics Processing Center 19/03/2018 8 006.31 ALP-I DCB3313 30/09/2024 02/04/2024 19/03/2018 Book  
        Dept. of Computational Biology and Bioinformatics Dept. of Computational Biology and Bioinformatics Processing Center 19/11/2018 6 006.31 ALP-I DCB3509 18/01/2022 18/01/2022 19/11/2018 Book 31/03/2022
        Dept. of Futures Studies Dept. of Futures Studies Processing Center 28/10/2021 3 006.31 ALP DFS4319 03/11/2023 12/06/2023 28/10/2021 Book