Universität Wien

052323 VU Probabilistic Artificial Intelligence (2025S)

3.00 ECTS (2.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle
Mo 16.06. 13:15-14:45 Seminarraum 5, Währinger Straße 29 1.UG

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 03.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 10.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 17.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 24.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 31.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 07.04. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 28.04. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 05.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Wednesday 07.05. 09:45 - 11:15 Hörsaal 41 Gerda-Lerner Hauptgebäude, 1.Stock, Stiege 8
  • Thursday 08.05. 09:45 - 11:15 Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6
  • Monday 12.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 19.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Tuesday 20.05. 09:45 - 11:15 Hörsaal 50 Hauptgebäude, 2.Stock, Stiege 8
  • Monday 26.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 02.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Wednesday 18.06. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Monday 23.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Monday 30.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG

Information

Aims, contents and method of the course

This course is about building systems that can perform well in uncertain situations without prescribing the optimal behavior. To this end the course covers the fundamentals as well as state-of-the-art methods for building probabilistic artificial intelligence systems like Bayesian modeling, variational inference, generative models (variational auto encoders, diffusion models), and reinforcement learning.
Programming assignments complement the lectures. Upon successful participation in the course, students will understand the fundamentals of probabilistic artifical intelligence, know how to apply basic methods in theory and practice, and will have the ability to connect to current research.

Assessment and permitted materials

* Written exam (individual work): at the end of the semester; you will be allowed to bring a handwritten A4 sheet (2 pages) of notes.
* Programming assignments (individual or group work): you will solve programming assignments at home; you will have to submit your executable source code and a written report describing the results obtained with your implementation.
* Paper presentation (individual or group work): You will choose a research paper on one of the course topics, understand it carefully, and present the key ideas to the course participants. The topic can be chosen from a list of selected papers published at the beginning of the course. Students are welcome to suggest research papers they wish to work on, but an instructor's prior agreement must be obtained.

Minimum requirements and assessment criteria

Solid knowledge in mathematics, probability theory, and algorithms is required to take the course. Solid coding skills are expected. Experience with deep learning frameworks like PyTorch or TensorFlow is a strong plus. You should also have taken an introductory course to machine learning or artificial intelligence so that you know for instance the basics of supervised learning and probabilistic modeling.

Examination topics

All topics covered in class, the reading material, and the exercises. Referenced literature (as indicated in detail on the lecture slides).

Reading list

Association in the course directory

Last modified: We 11.06.2025 13:05