Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society


The Elements of Statistical Learning I (WS 2007/2008)


The course Satistical Learning II will be given in the coming Summer Semester. It will cover advanced methods in supervised and unsupervised learning followed by interesting applications that use these methods for solving challenging problems.

The second exam is scheduled for Wednesday, 16th of April 2008 (2 - 4:30 p.m.). If you want to participate please send an E-mail to the tutor with your name, matriculation number and the preferred language in which you want to take the exam (English or German).

The first exam is scheduled for Wednesday, 27th of February 2008 (10 - 12 a.m.). If you want to participate please send an E-mail to the tutor with your name, matriculation number and the preferred language in which you want to take the exam (English or German).

Please don't forget to register for the course. You can do that by following this link. The registration is OBLIGATORY for students that want to take the exam! The DEADLINE for registration is 01.12.2007!
More details about the registration are available here.

The first tutorial will most probably take place Friday, 9th of November 2007, 10-12h, Room 023 (MPI building).
The first lecture will take place Wednesday, 31th October 2007, 10:00, Room 024 (MPI Building).

Check this site frequently for updates and announcements.

Teacher: Thomas Lengauer

Tutor: Jasmina Bogojeska

Language: English

Time and location:

Lecture: Wednesday, 10:00 - 12:00, Building 46, Room 024 (MPI Building)
Starting October 31th, 2007
Last lecture on February 20th, 2008
Tutorial: Every second Friday, 10-12h, room 023, MPI building.
The first tutorial will be given on November 9th, 2007.
Office hours:
Thomas Lengauer: after each lecture
Jasmina Bogojeska: Friday, 09-10 h, Building 46, Room 526 (MPI Building) - or by appointment.


This course covers a subject that is relevant for computer scientists in general as well as for other scientists involved in data analysis and modelling. It is not limited to the field of computational biology.
The course will be the first part of a two semester course on Statistical Learning. The first part (WS 2007/2008) will concentrate on chapters 1-5 and 7-10 of the book The Elements of Statistical Learning, Springer 2001. In both semesters, there will be two hours of lecture per week and one hour of tutorial (V2/Ü1), however, the tutorial will actually be two hours every second week.
Both parts of this lecture fulfil the requirements for the curricula of computer science and bioinformatics as optional course with 5 credit points (Spezialvorlesung, 5 Leistungspunkte).


The course is targeted to advanced students in math, computer science and general science with mathematical background. Students should know linear algebra and have basic knowledge of statistics.

Requirements for the course certificate:

You need a cumulative 50% of the points in the homework assingments to be admitted to the oral exam. A score of 50% in the exam is then considered a passing grade.


Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer 2001. The readers of the course are encouraged to acquire this book.
More information on this book, as well as a contents listing can be found here.


The tutorial focuses on both, the material presented in the lecture and the homework assignments. Usually, a very brief reiteration of parts of the lecture is given; the focus will be on the last assignment, though. Homework assignments will cover theoretical proofs and programming excercises with roughly equal weight.
The programming language that we use is R - a language for statistical computing. It is freely available for Windows and Linux and - as a vectorized programming language - is ideally suited for the problems we will encounter. There are also many freely available packages (or libraries) to perform a variety of classification and regression tasks, or to visualize the results of statistical analyses in a convenient way.

Course material:

Programming resources are now listed in the homework handouts section.

Search MPII (type ? for help)