Introduction to Machine Learning

Introduction to Machine Learning

Introduction to Machine Learning:

Author: Alpaydin Ethem 

Publisher: PHI Learning Pvt. Ltd

Language: English

Pages: 640 pages

About The Author:


Introduction to Machine Learning – Ethem ALPAYDIN ​​did his BSc from the Computer Engineering Department of Bogazi University in 1987 and received his doctorate in science degree from the Ecole Polytechnique Federale de Lausanne in 1990. He did postdoctoral work at the International Computer Science Institute, Berkeley in 1991 and was appointed thereafter. Assistant Professor in the Department of Computer Engineering of Bogaziki University. He was appointed Associate Professor in 1996 and Professor in the same department in 2002. He took early retirement in January 2019.

About This Book:


This textbook provides a single source introduction to the primary approach to machine learning. It is for advanced undergraduate and graduate students, as well as developers and researchers in the field. No prior background has been assumed in artificial intelligence or statistics. Several key algorithms discussed in the book, example date sets and project-oriented home work assignments are accessible through the World Wide Web.

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Preface:

  • 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. Multiplayer 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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