Universität Wien

052323 VU Probabilistic Artificial Intelligence (2025S)

3.00 ECTS (2.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung
Moodle
Mo 28.04. 13:15-14:45 Seminarraum 5, Währinger Straße 29 1.UG

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Montag 03.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 10.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 17.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 24.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 31.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 07.04. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 05.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 12.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 19.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 26.05. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 02.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 16.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 23.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
  • Montag 30.06. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

* 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.

Mindestanforderungen und Beurteilungsmaßstab

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.

Prüfungsstoff

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

Literatur

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Do 13.03.2025 11:25