Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

401-3632-00L  Computational Statistics

SemesterSpring Semester 2015
LecturersM. Mächler, P. L. Bühlmann
Periodicityyearly recurring course
Language of instructionEnglish

Catalogue data

Abstract"Computational Statistics" deals with modern methods of data analysis (aka "data science") for prediction and inference. An overview of existing methodology is provided and also by the exercises, the student is taught to choose among possible models and about their algorithms and to validate them using graphical methods and simulation based approaches.
ObjectiveGetting 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.
ContentCourse Synopsis:
multiple regression, nonparametric methods for regression and classification (kernel estimates, smoothing splines, regression and classification trees, additive models, projection pursuit, neural nets, ridging and the lasso, boosting). Problems of interpretation, reliable prediction and the curse of dimensionality are dealt with using resampling, bootstrap and cross validation.
Details are available via .

Exercises will be based on the open-source statistics software R ( Emphasis will be put on applied problems. Active participation in the exercises is strongly recommended.
More details are available via the webpage (-> "Computational Statistics").
Lecture noteslecture notes are available online; see (-> "Computational Statistics").
Literature(see the link above, and the lecture notes)
Prerequisites / NoticeBasic "applied" mathematical calculus and linear algebra.
At least one semester of (basic) probability and statistics.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits10 credits
ExaminersM. Mächler
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 180 minutes
Additional information on mode of examinationPrüfung enthält Aufgaben am Computer mit Benützung von R
Written aidsEin A4-Blatt doppelseitig handgeschriebene Zusammenfassung. One sheet of paper (A4, front and back) with a hand-written summary.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkLecture notes "Computational Statistics" (C) Martin Maechler and Peter Buehlmann
Only public learning materials are listed.


401-3632-00 VComputational Statistics3 hrs
Thu13-15HG G 3 »
Fri09-10HG E 1.2 »
M. Mächler, P. L. Bühlmann
401-3632-00 UComputational Statistics
In the first week *only*, the exercises will be in a computer lab; on how to use R on these computers (will be used for exam, as well).
2 hrs
Fri10-12HG E 1.2 »
20.02.10-12HG E 26.1 »
10-12HG E 26.3 »
M. Mächler, P. L. Bühlmann


No information on groups available.


There are no additional restrictions for the registration.

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