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Table of contents





Statistical foundations of machine learning


Introduction


Foundations of probability


Classical parametric estimation


Nonparametric estimation and testing


Statistical supervised learning


The machine learning procedure


Linear approaches


Nonlinear approaches


Model averaging approaches


Feature selection


Conclusions


Bibliography


Appendix




Recently updated pages




The PRESS statistic  - 29 Oct 2015  

Permutation test  - 23 Feb 2014  

Randomization tests  - 23 Feb 2014  

The bootstrap principle  - 23 Feb 2014  

Bootstrap estimate of bias  - 23 Feb 2014  

Bootstrap sampling  - 23 Feb 2014  

Bootstrap  - 23 Feb 2014  

Estimation of arbitrary statistics  - 23 Feb 2014  

Nonparametric methods  - 23 Feb 2014  

Receiver Operating Characteristic curve  - 23 Feb 2014  




Statistics : Total views




Statistical foundations of machine learning  - 11,532   Bias of the estimator σ^2  - 2,796   
The sum of i.i.d. random variables  - 4,519   Plug-in principle to define an estimator  - 2,753   
Introduction  - 4,412    Bivariate normal distribution  - 2,742   
Modelling from data  - 4,186   Recursive least-squares  - 2,431   
USPS dataset  - 4,092    Stacked regression  - 2,359   
Bias/variance decomposition of MSE  - 3,288   The random model of uncertainty  - 2,264   
Axiomatic definition of probability  - 3,285   The PRESS statistic  - 2,253   
Bias and variance of μ^  - 3,166   Classification and Regression Trees  - 2,250   
Foundations of probability  - 3,055   Bias and variance  - 2,250   
Stastistical machine learning  - 2,802   Normal distribution: the scalar case  - 2,202  

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