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


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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