Introduction To Machine Learning (Record no. 293608)

MARC details
000 -LEADER
fixed length control field 02028nam a2200157Ia 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 8120327918
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 ETH-I
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ethem Alpaydin
245 ## - TITLE STATEMENT
Title Introduction To Machine Learning
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. Prenice- Hall of India
Date of publication, distribution, etc. 2005
300 ## - PHYSICAL DESCRIPTION
Extent 415 pages : illustrations
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Hidden Markov models -- Assessing and comparing classification algorithms -- Combining multiple learners -- Reinforcement learning -- Probability.
520 ## - SUMMARY, ETC.
Summary, etc. The goal of machine learning is to program computers to optimize a performance criterion using example data or past experience. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict consumer behaviour, recognize faces or spoken speech, optimize robot behaviour, etc so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. This is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial Intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book is intended for senior graduate and post graduate level courses on machine learning and it should be of great interest to engineers working in the field concerned with the application of machine learning methods . The prerequisites are courses on computer programming, probability, calculus and linear algebra.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning. Apprentissage automatique. Aprendizado computacional.
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 Checked out Date last seen Date last checked out Price effective from Koha item type
        Dept. of Computational Biology and Bioinformatics Dept. of Computational Biology and Bioinformatics Processing Center 01/09/2015 3 006.31 ETH-I DCB180 23/08/2024 23/08/2024 23/08/2024 19/07/2019 Book