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

The exams will be oral.

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