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Machine Learning
book

Machine Learning

by Sergios Theodoridis
April 2015
Intermediate to advanced content levelIntermediate to advanced
1062 pages
40h 35m
English
Academic Press
Content preview from Machine Learning
Chapter 3

Learning in Parametric Modeling

Basic Concepts and Directions

Abstract

The chapter presents an overview of basic directions in machine learning and the basic notions related to parametric modeling are introduced. The tasks of regression and classification are defined and basic concepts related to parameter estimation are outlined such as estimator efficiency, Cramér-Rao bound, sufficient statistic. The least-squares estimator and some of its properties are discussed. The notions of inverse problems, overfitting, bias-variance dilemma and regularization are presented. The methods of maximum likelihood, maximum a posteriori and Bayesian inference are introduced. Finally, the curse of dimensionality and the cross-validation technique ...

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Publisher Resources

ISBN: 9780128015223