Probabilistic Foundations of Artificial Intelligence

How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit ”intelligent” behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students. VVZ Information is available here.

Instructor Prof. Andreas Krause
Head TA Adish Singla
Assistants Yuxin Chen, Felix Berkenkamp, Jens Witkowski, Diego Ballesteros
Mailing List If you have any questions please send them to from your address.
Friday 10-12 CHN C 14
Friday 13-14 CHN C 14
Friday 14-15 CHN C 14

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  • First class on 18.9. First tutorial/recitation on 25.9.
The mode of examination is written, 120 minutes length. The language of examination is English. As written aids, you can bring one A4 sheet of paper (you can write on both sides), either handwritten or 11 point minimum font size. Please bring your Legi (ID card) for the exam. Please do not use cellphones / tablets in the exam. Calculators are not allowed.
You can find previous exams here: [2014], [2013], [2012].
Text Books
  • S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. 3rd edition
  • Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2007 (optional)