|Teaching Assistant||Fabian Müller|
Wednesday, 10:00 - 12:00, Campus E2.1 (CBI building), room 001
First lecture will be held on April 13, 2011 in E2.1, room 001
Thursday (biweekly), 16:00 - 18:00, Campus E2.1 (CBI building), room 007
Thursday (biweekly), 18:00 - 20:00, Campus E2.1 (CBI building), room 007
First tutorial will be held on April 14, 2011
Thomas Lengauer: after each lecture
Fabian Müller: Tuesday 17:00-18:00 or by appointment, Campus E1.4 (MPI building), Room 509
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 2011) 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 tutorial will actually be two hours every other week.
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.
Problem sets will cover theoretical proofs and programming exercises with roughly equal weight. They are due Wednesday before the lecture (10:00 sharp) every other week and can be handed in in groups of two students. The first assignment will be handed out after 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.