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Seminar for Statistics
 
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Advanced Topics in Computational Statistics

Dr. Martin Maechler, Prof. Nicolai Meinshausen Lecture: Th 8-10, HG D 3.2

Course Synopsis

Nearest Neighbours, Trees, Curse of Dimensionality and Fast Approximations
Unsupervised learning and clustering algorithms

(EM algorithm for mixed models; medoids, hierarchical methods, validation)

Lecture notes                                        

R Scripts as used in the Lecture

A selection is online in this ftp  directory.

Recommended Reading

T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning. Springer
(freely available as E-book from the ETH library)

J. E. Gentle. Elements of Computational Statistics. Springer

W. N. Venables, B. D. Ripley. Modern Applied Statistics with S. Springer

 

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© 2016 Mathematics Department | Imprint | Disclaimer | 6 November 2013
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