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.
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- The materials from the exam review session have been posted.
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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: 
- S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. 3rd edition
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2007 (optional)