|Teaching Assistant||Peter Ebert|
Wednesday, 10:00 c.t. - 12:00, Campus E2.1 (CBI building), room 001
First lecture will be held on April 18, 2012 in E2.1, room 001
Monday, 12:00 - 14:00, MPI, room 022
Tuesday, 14:00 - 16:00, MPI, room 023
Thomas Lengauer: after each lecture
Peter Ebert: By appointment, Campus E1.4 (MPI building), Room 508
In order to successfully participate, you need to register for the lecture in the LSF/HISPOS system of Saarland University - this will be possible as soon as the exam date has been entered into the system (this usually happens a few weeks into the semester). Additionally, please write an e-mail to the teaching assistant:
|Subject line:||[SL1] Registration|
|Body:||Last name, first name
official e-mail address*
*this means: mail account from Saarland University, the CBI, the MPI or similar
**e.g. bioinformatics, CS
Lecture slides, tutorial handouts and problem sets are available in the password protected area.
This course covers a subject that is relevant for computer scientists in general as well as for other scientists involved in data analysis and modeling. 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 (SS 2012) will concentrate on chapters 1-5 and 7-10 of the book The Elements of Statistical Learning, Springer (second edition, 2009). In both semesters, there will be two hours of lecture per week and one hour of tutorial (V2/Ü1); however, the slot for the tutorial will be set after the first lecture, a 2 hour tutorial every other week is also possible.
Both parts of this lecture fulfill the requirements for the curricula of computer science and bioinformatics as special lecture (Spezialvorlesung, 5 credit points).
The course is targeted to advanced students in bioinformatics, computer science, math 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 problem sets 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 (second edition, 2009). 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 on the Springer web site.
Additional literature can be found in the library; the reserve list for the lecture can be found here: library reserve list for 'Elements of Statistical Learning 1'
Please keep in mind that only the book by Hastie, Tibshirani and Friedman will be covered in the lecture.
Problem sets will cover theoretical proofs and programming exercises with roughly equal weight. In general, they are due Wednesday before the lecture (10:00 sharp - exceptions possible for the two lectures on Friday); further details regarding the assignments will be announced in the first lecture.
The programming language that will be used is R - a language for statistical computing. It is freely available for Windows, Linux and Mac. 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.
The tutorials focus on the problem sets. A very brief reiteration of parts of the lecture is also given.