263-5210-00L Probabilistic Artificial Intelligence
Semester | Autumn Semester 2022 |
Lecturers | A. Krause |
Periodicity | yearly recurring course |
Language of instruction | English |
Courses
Number | Title | Hours | Lecturers | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
263-5210-00 V | Probabilistic Artificial Intelligence Fr 10-12 und 13-14 im ETA F5 mit Videoübertragung ins ETF E1 | 3 hrs |
| A. Krause | ||||||||||||
263-5210-00 U | Probabilistic Artificial Intelligence Q&A session: Monday, 17-18, via zoom | 2 hrs |
| A. Krause | ||||||||||||
263-5210-00 A | Probabilistic Artificial Intelligence | 2 hrs | A. Krause |
Catalogue data
Abstract | This 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. |
Learning objective | How 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. |
Content | Topics 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 / Notice | Solid 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 credits | 8 credits |
Examiners | A. Krause |
Type | session examination |
Language of examination | English |
Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |
Mode of examination | written 120 minutes |
Additional information on mode of examination | 70% 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. |
Written aids | Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. Simple non-programmable calculator. |
Digital exam | The exam takes place on devices provided by ETH Zurich. |
This information can be updated until the beginning of the semester; information on the examination timetable is binding. |
Learning materials
Main link | Information |
Only public learning materials are listed. |
Groups
No information on groups available. |
Restrictions
Places | 900 at the most |
Waiting list | until 03.10.2022 |