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 Decision 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.

There is a requirement for participating in the exams. Once during the semester, you need to present the solution of a homework problem in one of the tutorials. If you would like to present a solution, please book a presentation slot using the booking link provided on the exercise sheet. Before presenting in the tutorial, you must attend a short (10–15 minute) meeting to discuss your solution. A separate booking list for these meeting slots is also available on the exercise sheets.

homework01.pdf
homework02.pdf
homework03.pdf
homework04.pdf
homework05.pdf
homework06.pdf
homework07.pdf
homework08.pdf

tutorial01.pdf
tutorial02.pdf
tutorial03.pdf
tutorial04.pdf
tutorial05.pdf
tutorial06.pdf
tutorial07.pdf