Probabilistic Artificial Intelligence (Fall ’17)

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.
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  • First class on Sep 22; first tutorial on Sep 29.
  • The lecture video recordings are available at ETH Videoportal.
  • Tutorials on Nov 10 will take place as normal.
  • No lecture/tutorials on Dec 8.

Instructor Prof. Andreas Krause
Head TA Alkis Gotovos
Assistants Carlos Cotrini, Sebastian Curi, Hoda Heidari, Johannes Kirschner, Jens Witkowski
Mailing List If you have any questions please send them to from your address.
Friday 10-12 HG E7
Friday 13-14 CHN C14
Friday 14-15 CHN C14

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: [2016], [2015], [2014], [2013], [2012].
Text Books
  • S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (3rd edition).
  • Christopher M. Bishop. Pattern Recognition and Machine Learning. [optional]
  • Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction.