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2. Machine learning : the new AI [2016]
 Alpaydin, Ethem, author.
 Cambridge, Massachusetts : The MIT Press, [2016] [Piscataqay, New Jersey] : IEEE Xplore, [2016]
 Description
 Book — 1 online resource (xv, 206 pages).
 Summary

A concise overview of machine learningcomputer programs that learn from datawhich underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognitionas well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learningthe foundation of efforts to process that data into knowledgehas also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from numbercrunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science, " and discusses the ethical and legal implications for data privacy and security.
(source: Nielsen Book Data)
3. Introduction to machine learning [2014]
 Alpaydin, Ethem, author.
 Third edition.  Cambridge, Massachusetts : The MIT Press, [2014]
 Description
 Book — 1 online resource (xxii, 613 pages) : illustrations.
 Summary

 Introduction
 Supervised learning
 Bayesian decision theory
 Parametric methods
 Multivariate methods
 Dimensionality reduction
 Clustering
 Nonparametric methods
 Decision trees
 Linear discrimination
 Multilayer perceptrons
 Local models
 Kernel machines
 Graphical models
 Brief contents
 Hidden markov models
 Bayesian estimation
 Combining multiple learners
 Reinforcement learning
 Design and analysis of machine learning experiments.
(source: Nielsen Book Data)
4. Introduction to machine learning [2014]
 Alpaydin, Ethem, author.
 Third edition.  Cambridge, Massachusetts : The MIT Press, [2014]
 Description
 Book — 1 online resource (xxii, 613 pages) : illustrations.
 Summary

 Introduction
 Supervised learning
 Bayesian decision theory
 Parametric methods
 Multivariate methods
 Dimensionality reduction
 Clustering
 Nonparametric methods
 Decision trees
 Linear discrimination
 Multilayer perceptrons
 Local models
 Kernel machines
 Graphical models
 Brief contents
 Hidden markov models
 Bayesian estimation
 Combining multiple learners
 Reinforcement learning
 Design and analysis of machine learning experiments.
(source: Nielsen Book Data)
 Alpaydin, Ethem.
 2nd ed.  Cambridge, Mass. : MIT Press, c2010.
 Description
 Book — 1 online resource (xl, 537 p.) : ill.
 Summary

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of 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. In order to present a unified treatment of machine learning problems and solutions, 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 text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)
 Alpaydin, Ethem.
 2nd ed.  Cambridge, Mass. : MIT Press, ©2010.
 Description
 Book — 1 online resource (xl, 537 pages) : illustrations.
 Summary

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of 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. In order to present a unified treatment of machine learning problems and solutions, 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 text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)
 Berlin ; Heidelberg : SpringerVerlag Berlin Heidelberg, 2003.
 Description
 Book — 1 online resource
 Summary

 Learning Algorithms
 SVM and Kernel Methods
 Statistical Data Analysis
 Pattern Recognition
 Vision
 Speech Recognition
 Robotics and Control
 Signal Processing
 TimeSeries Prediction
 Intelligent and Hybrid Systems
 Neural Network Hardware
 Cognitive Science
 Computational Neuroscience
 Special Sessions
 ComplexValued Neural Networks: Theories and Applications
 Computational Intelligence and Applications
 Emotional Recognition
 Neural Networks for Bioinformatics Applications.
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