* Lectures: Tuesdays 16:00, Thursdays 09:00, starting 19.04.2007
* Topics: Kernels, Neural Networks, Clustering, applications in Bioinformatics
* Tutor: Laura Tolosi
The next tutorial will take place Friday, 9th February 2007, same room, 09:15. We will have a discussion meeting on Thursday, 8th February, Rotunda 5th floor, 16:00 .
Every Thursday, 16:00, the weeks with no tutorial, there will be an extra discussion hour for those who are interested. The room is to be soon anounced (the ground floor, MPI building).
The first lecture will take place Wednesday, 18th October, 11:00, Building 46, Room 024.
Check this site frequently for updates and announcements.
Teacher: Thomas Lengauer
Tutor: Laura Tolosi
Wednesday, 11:00 - 12:45, Building 46, Room 024 (MPI Building)
Starting October 18th, 2006
Last lecture on February 14th, 2007
|Tutorial:||Every second Friday, 9-11h, room 021, MPI building, starting 27th October
Every second Thursday, 16-17h, MPI building, starting 30th November
- Wednesday, 14th February 2007, 15:00 - 15:30
- Wednesday, 7th March 2007, 10:30 - 13:30
- Wednesday 18th April 2007, 14:45 - 17:00
The exam is oral, it involves groups of 3 students and takes 45 min per group.
You may find this information useful: Exam guidelines
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 2006/2007) 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 6 resp. 4 credit points (Spezialvorlesung, 6 bzw. 4 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.
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.