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.
- The files are password protected. To obtain the password you need to be inside the ETH network and click here. To establish a VPN connection click here.
- 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.
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: 
- 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.