Computational Statistics
Spring semester 2019
General information
Lecturer  Marloes Maathuis 

Assistants  Jeffrey Näf, Jinzhou Li, Domagoj Ćevid 
Lectures  Thu 1315
HG
F 3
>>
Fri 0910 HG G 3 >> 
Exercises  Fri 1011 HG F 3 >> 
Question hour  Fri 1112 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 80% 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 resampling based methods for inference. The course is handson, and methods are applied using the statistical programming language R.
The material for each week will be indicated under the Course material tab (reading material, slides and Rcode). See also the Literature tab for the main books that we will use. Exercises and related material can be found under the Exercise 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.
All discussions and announcements regarding the course can be found in the Moodle forum.
Course material
Click here to download all materials from the lectures.
Week  Topic 

Week 1  Linear regression 
Week 2  Linear regression 
Week 3  Confidence intervals and Biasvariance tradeoff 
Week 4  kNN, Cross validation 
Week 5  Bootstrap

Week 6  Bootstrap 
Week 7  Bootstrap and Simulation tests 
Week 8  Permutation tests and Multiple testing 
Week 9  Model Selection 
Week 10  Model selection and inference after model selection 
Week 11  Beyond linearity 
Week 12  Tree based methods 
Week 13  Bagging and Random forest 
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. During the exercise hour, the assistants will discuss the last series and introduce the new series, often showing some example code as well. There is a "Präsenzstunde" directly following the exercise hour, which is meant to work on the exercises yourself and to ask questions about anything related to the exercises. For example, you can ask about the solutions for last week's series, or get help with the current series. We will make sure that there are several assistants 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 email the solved exercises to compstat@stat.math.ethz.ch by 10am on Friday, a week after the prediscussion.
Click here to download all materials from the exercise classes.
Exercise sheets
The new exercise sheet will be uploaded on Tuesday preceding the corresponding session.
Exercises  Discussion  Deadline  Materials 

R Tutorial  February 22, 2019  RTutorial, R Code  
Series 1 (updated March 1)  March 1, 2019  March 8, 2019  R Code 
Series 2  March 8, 2019  March 15, 2019  R Code 
Series 3  March 15, 2019  March 22, 2019  R Code 
Series 4  March 22, 2019  March 29, 2019  R Code 
Series 5 (updated August 14), R Skeleton  March 29, 2019  April 5, 2019  R Code 
Series 6  April 5, 2019  April 12, 2019  R Code 
Series 7, Data  April 12, 2019  May 3, 2019  R Code 
Series 8  May 3, 2019  May 10, 2019  R code 
Series 9  May 10, 2019  May 17, 2019  R code 
Series 10  May 17, 2019  May 24, 2019  R code 
Series 11  May 24, 2019  May 31, 2019  R code 
Solutions
The solutions will be sent weekly to the students enrolled for this course.
Literature
Main literature
 P. Bühlmann, M. Mächler. Script Computational Statistics. (Version of October 12, 2016).
 G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning: with Applications in R [ISLR]. Springer.
 T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning [ESL]. Springer.
Other relevant literature
 C. R. Shalizi. Advanced Data Analysis from an Elementary Point of View.
 B. Efron, T. Hastie. Computer Age Statistical Inference. Cambridge University Press.
 J. E. Gentle. Elements of Computational Statistics. Springer.
 W. N. Venables, B. D. Ripley. Modern Applied Statistics with S. Springer.
 L. Wasserman. All of statistics. Springer.