MA-INF 1218: Algorithms and Uncertainty


When Where Start Lecturer
Two lectures per week online, prerecorded April 12 Kesselheim

Q&A - Session

When Where Start Lecturer
Wednesday, 12:15-12:45 online April 14 Kesselheim


When Where Start Lecturer
Thursday, 10:15-11:45 online April 15 Braun
Thursday, 14:15-15:45 online April 15 Braun


The exams will be oral, about 25-30 minutes long, and take place as a video conference via Zoom. You will need to show and identify yourself on camera. It does not matter whether it is a computer or a smartphone. No other hardware is required. If you would like to take the exam but not in this form as a video conference, please contact us.

The first period will take place on August 2 to 4. Please contact Alexander Braun to be assigned a time slot until July 11. Please also state your first and second most preferred day for the exam. This year, you will receive a confirmation of the day of your exam on July 12 (e.g. your exam will be on Aug 2), the exact time will only be announced a few days before the exam (e.g. your exam will take place on Aug 2, 10:30). If you do not plan to take the exam in the first period, you can also be assigned a time slot for the second period already, which will be on September 9 and 10. Still, we also assign time slots for the second period in August.

One more thing: If you have been assigned a time slot but then decide to not take the exam, please remember to cancel it as soon as possible. It is very important for us to know that you will not show up because otherwise we will be waiting for you. In this case, send an e-mail to Thomas Kesselheim or Alexander Braun. Even a last-minute cancellation is better than a no-show. Of course, make sure that you also follow the official procedures (if applicable).


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

  • Online Algorithms
  • Online Learning Algorithms and Online Convex Optimization
  • Markov Decisions Processes
  • Stochastic and Robust Optimization


You should bring a solid background in algorithms, calculus, and probability theory. Specialized knowledge about certain algorithms is not necessary.

Problem Sets - Tutorials

Problem Sets - Homework

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