Probabilistic Artificial Intelligence (2019)

Probabilistic Artificial Intelligence (Fall ’19)

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 20; first tutorial on Sep 27.
  • The lecture video recordings are available at ETH Videoportal.
  • Starting from Homework 4, part of the homework will take place in the course Moodle.

Friday 10-12 HG E7 (Video-streaming in E3)
Friday 13-14 CHN C14
Friday 14-15 CHN C14
Friday 15-16 CHN C14

The exam is 120 minutes long. It might take place at a computer. 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. Ph. D. Students can only get credits by passing the exam. You can find previous exams here: [2018], [2017], [2016], [2015], [2014], [2013], [2012].
There will be a different homework fortnightly. These will not be corrected and do not count for the final grade. However, we recommend to do them as they will help preparing for the final exam and understanding the concepts.
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.