When | Where | Start | Lecturer |
---|---|---|---|

Monday, 10:15-11:45 Wednesday, 12:15-13:45 | online | April 20 | Kesselheim |

The lecture will start on April 20 as remote teaching. Please sign up to the eCampus course so that we can keep you posted. The recorded lectures are available on Sciebo. Please find the link in eCampus.

The exams will take place between in the weeks July 27-31 and Aug 31-Sep 4. Please do not forget to register. We will assign time slots only after the registration.

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.