The Elements of Statistical Learning
The Elements of Statistical Learning (WS 2015/16)
- 2016-03-16 Please check the re-exam schedule in the password protected area.
- 2016-03-16 Please check the exam schedule for the second exam date.
- 2016-02-12 Please check the exam schedule for the first exam.
- 2016-02-12 Please check if you are registered for the correct exam date in the password protected area.
- 2016-02-04 Recommended follow-up lecture: Statistical Learning in Computational Biology
- 2016-01-13 First oral exam dates: 18.02.2016 or 07.04.2016.
- 2016-01-13 Please, register in the Hispos/LSF system for your preferred exam date and write an email to the TA with your choice and your preferred exam language (German or English) latest two weeks before the exam date.
- 2016-01-08 New regulations for homework announced in password protected area
|Teaching Assistant||Anna Feldmann|
Time and location
Thursday or/and Friday, 10:00 - 12:00, Campus E2.1 (CBI building), Room 001
First lecture will be held on Oct. 22, 2015
Thomas Lengauer: after each lecture
Anna Feldmann: by appointment, Campus E1.4 (MPII), Room 527
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).
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 fulfills the requirements for the curricula of computer science and bioinformatics as special lecture (Spezialvorlesung, 5 credit points).
The course will convey the ability, given a data set, to choose an appropriate statistical method for analyzing it, to select the appropriate parameters for the statistical model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects will be covered.
The course will, by and large, follow the book An Introduction to Statistical Learning with Applications in R (2013). At times the course will take additional material from the book The Elements of Statistical Learning, Springer (second edition, 2009). The former book is the more introductory text, the latter book is more advanced. Both books are available as free PDFs. We encourage you, though, to acquire at least the first book in print. There will be on average one lecture per week (90 min) and one tutorial every two weeks (90 min). The slot for the tutorial will be set after the first lecture.
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.
Requirements for the course certificate
You need a cumulative 50% of the points in the problem sets (in both theoretical and programming exercises) to be admitted to the oral exam.
James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning with Applications in R (2013). The students of the course are encouraged to acquire this book.
Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer (second edition, 2009).
Both books are available online as free PDFs.
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'.
Problem sets will cover theoretical proofs and programming exercises with roughly equal weight. In general, submission is due the date stated on the problem set before the lecture starts (10:00 sharp); 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.
What can I do to prepare for the lecture?
- Refresh your knowledge on basic statistics. Basic linear algebra will also be useful.
- Familiarize yourself with the R programming language. You might find the following tutorials useful: