263-5210-00L  Probabilistic Artificial Intelligence

SemesterAutumn Semester 2023
LecturersA. Krause
Periodicityyearly recurring course
Language of instructionEnglish


263-5210-00 VProbabilistic Artificial Intelligence
Fr 10-12 und 13-14 im ETA F5 mit Videoübertragung ins ETF E1
3 hrs
Fri10:15-12:00ETA F 5 »
10:15-12:00ETF E 1 »
13:15-14:00ETA F 5 »
13:15-14:00ETF E 1 »
A. Krause
263-5210-00 UProbabilistic Artificial Intelligence
Q&A session via zoom
2 hrs
Thu16:15-18:00CHN C 14 »
16:15-18:00HG F 7 »
A. Krause
263-5210-00 AProbabilistic Artificial Intelligence2 hrsA. Krause

Catalogue data

AbstractThis course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics.
ObjectiveHow can we build systems that perform well in uncertain environments? 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 robotics. The course is designed for graduate students.
ContentTopics covered:
- Probability
- Probabilistic inference (variational inference, MCMC)
- Bayesian learning (Gaussian processes, Bayesian deep learning)
- Probabilistic planning (MDPs, POMPDPs)
- Multi-armed bandits and Bayesian optimization
- Reinforcement learning
Prerequisites / NoticeSolid basic knowledge in statistics, algorithms and programming.
The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersA. Krause
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examination70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own and has a bonus/penalty function.

The practical projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory.
Failing the project results in a failing grade for the overall examination of Probabilistic Artificial Intelligence (263-5210-00L).
Students who do not pass the project are required to de-register from the exam and will otherwise be treated as a no show.

Due to the number of registered students, the exam may be paper-based and will most likely take place on a Saturday. The mode of the exam (computer-based or paper-based) will be finalized in end of October, and the exam date will be announced in December.
Written aidsTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. Simple non-programmable calculator.
Online examinationThe examination may take place on the computer.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkInformation
Only public learning materials are listed.


No information on groups available.


Places900 at the most
Waiting listuntil 02.10.2023

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