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.
News
- The video recordings of the first week’s lectures are now available at ETH Videoportal.
- 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 23.9. First tutorial/recitation on 30.9.
- We ask students with last names that start with letters A–M to attend the first tutorial session, while the second one is for N–Z. In case of a conflict with your schedule you may attend the other tutorial.
- The gridworld demo can be downloaded from here.
- The review session will be held on the 31.1. from 16:00-18:00 in CHN C14. Please send precise questions with the subject line “PAI review session” to the mailing list until the 29th.
Lectures
Tutorials
| Friday 13-14 |
CHN C14 |
| Friday 14-15 |
CHN C14 |
Exam
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:
[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. Springer, 2007 (optional)
- Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd edition (in progress).
