Probabilistic Artificial Intelligence (2022)

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.
Instructor Prof. Andreas Krause
Head TA Jonas Rothfuss
Assistants TBD
Mailing List Please use Moodle for any questions regarding the course or ask your question in the lectures, tutorials or Q&A sessions. If you need to contact the Head TA or the lecturer directly, please send an email to Please think twice before you send an email though and make sure you read all information here carefully.
Fri 10-12 ETA F5 [ETF E1] Stream
Fri 13-14 ETA F5 [ETF E1] Stream
Thu 16-18 CHN C14 Stream
Questions & Answers
Mon 17-18 Virtual Zoom

We do not maintain a mailing list, but instead kindly request you to use Moodle to ask questions with regard to the course. Please ask your questions in the Moodle forum whose topic best fits your question. If you need to contact the Head TA directly, please send an email to (Update) instead of contacting me at my personal email address. I will not respond to requests sent to my personal email address. Please think twice before you send an email though and make sure you read all information here carefully. Based on previous experience, we received a lot of questions or requests that are resolvable with the information provided here.
The course includes a total of five projects. Projects are code assignments that require solving machine learning problems with methods taught in the course. For each project, you are allowed to work in a group of one to three students. It is your own responsibility to form a group and you can find teammates in the lectures or on Moodle. The first project (Task 0) will be ungraded; its purpose is to help you familiarize yourself with the code submission workflow. The remaining projects are graded and are accounted for in determining your final grade for the course.
We will publish a total of six (optional) homework assignments during the lecture series. The homework assignments will be published on this website and some questions from the homework assignment will additionally be made available as a Moodle quiz . These assignments are intended for you to apply and reinforce the material presented in the lecture and to get accustomed to the Moodle platform. You are encouraged, but not required to do the homework. Doing the homework assignments is not mandatory. Your performance in the homework will have no influence on your final grade. Homework assignments are expected to be published bi-weekly, with solutions following one week after or being directly visible in Moodle. The exact day and time a homework is being published may deviate slightly over the course of the semester.
Demos will be shown during the lecture and are made available to you here. They are hosted in a GitLab repository to which you need to be given access.
The exam will last for 120 minutes and might be computer-based (Moodle). The language of examination is English.
Performance Assessment
In order to pass this course and be allowed to sit the session examination, you need to pass the projects. This is, you need to achieve a project grade of 4 or higher. If you don’t pass the projects, you are required to de-register from the exam and will otherwise be treated as a no-show. The final grade is computed as a weighted average of the session exam grade (70%) and the project grade (30%). There are no special arrangements for PhD students who are taking this course. In order to obtain a “Testat”, you need a passing grade for the course. This is you need to pass the projects as described above, take the exam and achieve an overall passing grade (4 or higher) for the course. If you passed the projects last year, you still need to do the projects again this year. The project grade cannot be carried over from the previous year.
Text Books
  • S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (4th edition).
  • C. E. Rasmussen, C. K. I. Williams Gaussian Processes for Machine Learning.
  • Christopher M. Bishop. Pattern Recognition and Machine Learning. [optional]
  • Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction.