INTRODUCTION TO MACHINE LEARNING

ALPAYDIN

INTRODUCTION TO MACHINE LEARNING - JAICO PUB HOUSE 2008

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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INTRODUCTION TO MACHINE LEARNING

006.31 ALP-I
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