Introduction to machine learning /
Alpaydin, Ethem,
Introduction to machine learning / Ethem Alpaydin. - Third edition. - xxii, 613 pages : illustrations ; 24 cm. - Adaptive computation and machine learning .
Includes bibliographical references (page 203) and index.
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
9788120350786 9780262028189 (hardcover) 0262028182 (hardcover)
Machine learning.
006.31 / ALP
Introduction to machine learning / Ethem Alpaydin. - Third edition. - xxii, 613 pages : illustrations ; 24 cm. - Adaptive computation and machine learning .
Includes bibliographical references (page 203) and index.
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
9788120350786 9780262028189 (hardcover) 0262028182 (hardcover)
Machine learning.
006.31 / ALP