In many application scenarios, algorithms have to make decisions under some kind of uncertainty. This affects different kinds of problems. For example, when planing a route, a navigation system should take into consideration the traffic. Also, any machine-learning problem is about some kind of uncertainty. A random sample of data is used as a representative for the entire world.
In this course, we will get to know different techniques to model uncertainty and what approaches algorithms can use to cope with it. We will cover topics such as
There is a requirement for participating in the exams. Once during the semester, you need to present a solution for one of the homework exercises to your fellow students in the tutorial sessions. If you would like to present a solution in one of the tutorials, please send an email to Anna Heuser until Monday 10:00 pm including the task you would like to present in which of the tutorial sessions. Of course, sending the email earlier than Monday evening is also possible and recommended. After you have received the confirmation, we will schedule a short meeting (10-15min) for a quick pre-discussion on your solution.
The exams will be oral. Exam dates for the first exam period (tentative): 05.02.-07.02. and 13.02.-14.02. Exam dates for the second exam period (tentative): 18.03.-21.03.
You should bring a solid background in algorithms, calculus, and probability theory. Specialized knowledge about certain algorithms is not necessary.