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Introduction to machine learning / Ethem Alpaydin.

By: Material type: TextTextLanguage: English Series: Adaptive computation and machine learningEdition: Third editionDescription: xxii, 613 pages : illustrations ; 24 cmISBN:
  • 9788120350786
  • 9780262028189 (hardcover)
  • 0262028182 (hardcover)
Subject(s): DDC classification:
  • 006.31 23 ALP
Contents:
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 -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
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 A. Probability Index
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Item type Current library Collection Call number Status Date due Barcode
General Books General Books CUTN Central Library Generalia Non-fiction 006.31 ALP (Browse shelf(Opens below)) Checked out to Martin Martin (16030A) 05/08/2022 34119
General Books General Books CUTN Central Library Generalia Non-fiction 006.31 ALP (Browse shelf(Opens below)) Available 34120
General Books General Books CUTN Central Library Generalia Non-fiction 006.31 ALP (Browse shelf(Opens below)) Available 34121

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 -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

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

A. Probability

Index

Includes bibliographical references (page 203) and index.

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