401-3632-00L  Computational Statistics

SemesterSpring Semester 2017
LecturersM. Mächler, P. L. Bühlmann
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
401-3632-00 VComputational Statistics3 hrs
Thu13:15-15:00HG E 5 »
Fri09:15-10:00HG 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
Fri10:15-12:00HG E 1.2 »
M. Mächler, P. L. Bühlmann

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 Link .

Exercises will be based on the open-source statistics software R (Link). Emphasis will be put on applied problems. Active participation in the exercises is strongly recommended.
More details are available via the webpage Link (-> "Computational Statistics").
Lecture noteslecture notes are available online; see
Link (-> "Computational Statistics").
Literature(see the link above, and the lecture notes)
Prerequisites / NoticeBasic "applied" mathematical calculus (incl. simple two-dimensional) and linear algebra (including Eigenvalue decomposition) similar to two semester "Analysis" in an ETH (math or) engineer's bachelor.

At least one semester of (basic) probability and statistics, as e.g., taught in an ETH engineer's or math bachelor.

Programming experience in either a compiler-based computer language (such as C++) or a high-level language such as python, R, julia, or matlab. The language used in the exercises and the final exam will be R (Link) exclusively. If you don't know it already, some extra effort will be required for the exercises.

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.
Online examinationThe examination may take place on the computer.
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.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Certificate of Advanced Studies in Computer ScienceFocus Courses and ElectivesWInformation
Computational Biology and Bioinformatics MasterMethods of Computer ScienceWInformation
Computer Science MasterComputer Science Elective CoursesWInformation
Mathematics BachelorCore Courses: Applied Mathematics and Further Appl.-Oriented FieldsWInformation
Mathematics MasterCore Courses: Applied Mathematics and Further Appl.-Oriented FieldsWInformation
Micro- and Nanosystems MasterModelling and SimulationWInformation
Computational Science and Engineering MasterCore CoursesWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation