Computational Statistics

Spring semester 2018

General information

Lecturer Marloes Maathuis
Assistants Peter Hinz, Loris Michel, Claude Renaux
Lectures Thu 13-15 HG F 3 >>
Fri 09-10 HG G 3 >>
Exercises Fri 10-12 HG F 3 >>
Course catalogue data >>
Credit points In order to obtain ECTS credit points, you need to pass the official exam. In order to obtain ETH credit points (this only applies to doctoral students from certain departments), you need to solve and submit at least 70% of the exercises.

Course content

We will study modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. Special emphasis will be placed on re-sampling based methods for inference. The course is hands-on, and methods are applied using the statistical programming language R.

The main text for the course is the following script. We will also use material from books (see Literature). The material for each week will be indicated under the Course material tab.

The exam will cover all material that is discussed during the lectures and the exercise sessions. If you miss a class, please make sure to copy class notes from someone else.


  • First week
    Beginning of lecture: Thursday, 22/02/2018.

    Exercises start on 23/02/2018 at 10:15 in room HG F3, with a special introduction to the R software. Please bring a laptop. See the Exercises tab and further helpful links related to R.

  • Special dates:
    • Different room: The class on March 29th takes place in HG E 3.
    • Holidays: There will be no class on March 30th, April 5th, April 6th and May 10th.
    • Cancellation: The class and exercises on May 11th are cancelled.

Course material

Week Topic
Week 1 Linear regression
Week 2 Linear regression
Week 3 Confidence intervals, and bias-variance trade-off
Week 4 KNN, Cross validation
Week 5 Bootstrap
Week 6 Bootstrap
Week 7 no class
Week 8 Bootstrap and Monte Carlo tests
Week 9 Permutation tests
Week 10 Multiple testing and model selection
Week 11 Inference after model selection, moving beyond linearity
Week 12 no class
Week 13 Beyond linearity
Week 14 Tree-based methods
Week 15 Bagging and boosting

Exercise classes

The exercises form a very important part of the course. They are also important for exam preparation, since part of the exam requires the use of R. If possible, please always bring a laptop to the exercise classes. After a brief introduction by one of the assistants, you can work on the exercises yourself or with others, and ask any questions you may have (about the exercises or the course material). There will be several assistants available to answer your questions.

Unless you are a PhD student who requires ETH credit points, you do not have to hand in your exercises. We will provide solutions, and you are expected to check your own work. Please ask if anything is unclear, for example if you don't understand the solutions, or if you found a different solution.

PhD students who require ETH credit points should place the solved exercises in the corresponding tray in HG J68 on the due date by noon at the latest.

Exercise sheets

The new exercise sheet will be uploaded on Tuesday preceding the corresponding session.

Exercises Discussion Deadline Slides / Notes
February 23, 2018 R-Tutorial, R Code
Series 1 March 2, 2018 March 9, 2018 R Code for discussion
Series 2 (updated March 19) March 9, 2018 March 15, 2018 R Code for discussion
Series 3 (updated March 26) March 16, 2018 March 22, 2018 R Code for discussion (Shalizi, Appendix N)
Series 4 (updated March 26) March 23, 2018 March 29, 2018 R Code for discussion
Series 5, R-skeleton April 13, 2018 April 19, 2018 R Code for discussion
Series 6 April 20, 2018 April 26, 2018 R Code for discussion
Series 7 (updated May 18), data April 27, 2018 May 3, 2018 R Code for discussion
Series 8 May 4, 2018 May 17, 2018 R code (html) for discussion
Series 9 May 18, 2018 May 24, 2018 Subdifferentials
Series 10 (revised on 31.05.18) May 25, 2018 May 31, 2018
General discussion June 1, 2018


The solutions will be sent weekly to the students enrolled for this course.


Main literature

Other relevant literature