|Teaching Assistant||Fabian Müller|
Wednesday, 10:00 - 12:00, Campus E2.1 (CBI building), room 007
First lecture will be held on October 19, 2011 in E2.1, room 007
Thursday (biweekly), 16:00 - 18:00, Campus E1.4 (MPII), room 023
Thomas Lengauer: after each lecture
Fabian Müller: Tuesday 16:00-17:00 or by appointment, Campus E1.4 (MPII), Room 509
Lecture slides, tutorial handouts and problem sets are available in the password protected area.
The course will be the second part of a two semester course on Statistical Learning. The first part (SS 2009) concentrated on chapters 1-5 and 7-10 of the book The Elements of Statistical Learning, Springer (second edition, 2009). The second part will present the remaining bookchapters, focusing on advanced topics in supervised and unsupervised leaning, such as kernel methods, SVMs, neural networks, random forests and clustering. The theoretical models will be illustrated with interesting applications, out of which many are challenging problems in the field of bioinformatics. As in the previous semester, 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.
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.
Both parts of this lecture fulfill 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. Attendance of Statistical Learning I is recommended, however not required if a student has basic knowledge in machine learning
Theoretical assignments are handed out every other week and are due two weeks after. Additionally, there will be two programming assignments handed out during the semester. Theoretical problem sets will involve mathematical proofs as well as testing the understanding of methods presented in the lecture and their relations. The R statistical programming language is required for the programming assignments in which the methods presented in the lecture will be applied to real-world data.
You need a cumulative 50% points for each the theoretical problem sets and the programming assignments respectively to be admitted to the oral exam.
Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer 2009. The readers of the course are encouraged to acquire this book. You can download it as a PDF file from the dedicated page on Charlie Tibshirani's web site. More information on this book, as well as a contents listing can be found on the Springer web site.
The tutorials focus on the problem sets. A very brief reiteration of parts of the lecture is also given. 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.