Introduction to machine learning / Ethem Alpaydin.
Material type:
TextLanguage: English Series: Adaptive computation and machine learningEdition: Third editionDescription: xxii, 613 pages : illustrations ; 24 cmISBN: 9788120350786; 9780262028189 (hardcover); 0262028182 (hardcover)Subject(s): Machine learningDDC classification: 006.31 | Item type | Current library | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
General Books
|
CUTN Central Library
This is a searchable open catalogue of all library of the Central University of Tamil Nadu. |
Non-fiction | 006.31 ALP (Browse shelf(Opens below)) | Available | 34119 | |
|
General Books
|
CUTN Central Library
This is a searchable open catalogue of all library of the Central University of Tamil Nadu. |
Non-fiction | 006.31 ALP (Browse shelf(Opens below)) | Available | 34120 | |
|
General Books
|
CUTN Central Library
This is a searchable open catalogue of all library of the Central University of Tamil Nadu. |
Non-fiction | 006.31 ALP (Browse shelf(Opens below)) | Available | 34121 |
Browsing CUTN Central Library shelves, Shelving location: Generalia, Collection: Non-fiction Close shelf browser (Hides shelf browser)
|
|
|
|
|
|
|
||
| 006.31 AGG Machine learning for text. | 006.31 ALP Introduction to machine learning / | 006.31 ALP Introduction to machine learning / | 006.31 ALP Introduction to machine learning / | 006.31 FLA Machine learning : | 006.31 GER Hands-on machine learning with Scikit-Learn and TensorFlow : | 006.31 GOL Genetic algorithms in search, optimization, and machine learning |
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.
There are no comments on this title.