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401-3632-00L 10 Credits
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Computational Statistics

Computational Statistics (Rechnerorientierte Statistik)

Lecturers & Examiners: Prof. em. Dr. Martin Mächler, Prof. Dr. Peter L. Bühlmann
VVZ CR 4.15

Last Updated: 2026-02-05 15:09:58

Abstract

Getting to know modern methods of data analysis for prediction and inference.Learn to choose among possible models and about their algorithms.Validate them using graphical methods and simulation based approaches.

Objective

Getting to know modern methods of data analysis for prediction and inference. Learn to choose among possible models and about their algorithms. Validate them using graphical methods and simulation based approaches.

Content

Course Synopsis: multiple regression, nonparametric methods for regression and classification (kernel estimates, smoothing splines, regression and classification trees, additive models, projection pursuit, neural nets), curse of dimensionality, resampling, bootstrap, cross validation. Details are available via http://stat.ethz.ch/teaching/lectures/ Exercises will be based on the open-source statistics software R ( http://www.R-project.org/ ). Emphasis will be put on applied problems. Active participation in the exercises is strongly recommended. More details are available via the webpage http://stat.ethz.ch/teaching/lectures/ .

Resources

Lecture Notes

will be distributed (in parts)

Literature

(see the link above, and the lecture notes)

General Information

Language
English
Frequency
Yearly recurring

Examination

Type
session examination
Mode
oral 30 minutes

Course Components

Type Title Time & Place Hours
lecture Rechnerorientierte Statistik (Computational Statistics)
  • Thu 13:15-15:00 (HG F 3)
  • Fri 09:15-10:00 (HG D 7.1)
  • 07.04 Date 10:15-12:00 (HG E 26.1)
3 h weekly
exercise Rechnerorientierte Statistik (Computational Statistics)
  • Fri 10:15-12:00 (HG D 7.1)
2 h weekly

Offered In