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)
- 006.31 23 ALP
Item type | Current library | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
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 | CUTN Central Library Generalia | Non-fiction | 006.31 ALP (Browse shelf(Opens below)) | Checked out to Soumya Karmakar (P231318) | 02/04/2024 | 34120 |
General Books | CUTN Central Library Generalia | Non-fiction | 006.31 ALP (Browse shelf(Opens below)) | Available | 34121 |
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006.305 FRE Artificial Intelligence and Playable Media / | 006.31 ADR Introduction to Deep Learning / | 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 FEN Machine Learning with Python for Everyone / |
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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