Universität Wien
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059100 VO Machines That Understand? Large Language Models and Artificial Intelligence (2024W)

5.00 ECTS (2.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Moodle
Th 23.01. 16:45-18:15 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7

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Details

Language: German, English

Examination dates

Lecturers

Classes (iCal) - next class is marked with N


03.10.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Benjamin Roth - Foundations of LLMs 1

10.10.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Benjamin Roth - Foundations of LLMs 2

17.10.2024 16:45 - 18:15 BIG-Hörsaal Hauptgebäude
Benjamin Roth - Foundations of LLMs 3

24.10.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Inverted Classroom Discussion
Alexander Koller - ChatGPT does not really understand you, does not really know anything, but is still revolutionary AI

31.10.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Andreas Stephan - Practical Session

07.11.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Terra Blevins - [Invited Talk]

14.11.2024 16:45 - 18:15 Hörsaal 33
Žiga Škorjanc - [Invited Talk]

21.11.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Dagmar Gromann - [Invited Talk]

28.11.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Michael Wiegand - [Invited Talk]

05.12.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Timour Igamberdiev - [Invited Talk]

12.12.2024 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Inverted Classroom Discussion
Asia Biega - Data Protection in Data-Driven Systems

09.01.2025 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Paul Röttger - [Invited Talk]

16.01.2025 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Mateusz Malinowski - [Invited Talk]

23.01.2025 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Q&A Session

30.01.2025 16:45 - 18:15 Hörsaal 33 Hauptgebäude
Exam

  • Thursday 03.10. 16:45 - 18:15 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 10.10. 16:45 - 18:15 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 17.10. 16:45 - 18:15 BIG-Hörsaal Hauptgebäude, Tiefparterre Stiege 1 Hof 1
  • Thursday 24.10. 16:45 - 18:15 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 31.10. 16:45 - 18:15 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 07.11. 16:45 - 19:00 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 14.11. 16:45 - 19:00 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 21.11. 16:45 - 19:00 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 28.11. 16:45 - 19:00 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 05.12. 16:45 - 18:15 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 12.12. 16:45 - 18:15 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 09.01. 16:45 - 19:00 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7
  • Thursday 16.01. 16:45 - 19:00 Hörsaal 33 Hauptgebäude, 1.Stock, Stiege 7

Information

Aims, contents and method of the course

The aim of the lecture series is to make current developments in the field of generative artificial intelligence understandable and to stimulate an informed dialogue about the capabilities, limitations and societal relevance of these models.

In the first units of the lecture, the basics of openly available (open source) language models are discussed. An introduction to the functionality and evaluation of language models is given, and questions such as those relating to training data are discussed.

The following units consist of lectures by researchers in the field of artificial intelligence. Some of the lectures are recordings of lectures from the last iteration of the lecture series, which will be made available via Moodle, and questions about these lectures are discussed in person in discussion sessions (flipped classroom). In other lectures, researchers are invited to present their current research in person to a broad university public. In addition to technical aspects, topics will include questions of fairness and responsibility in AI models, and the importance of AI for the broader university context, e.g. in the field of digital humanities.

One week before each lecture, the participants are given a reading recommendation with background information for the following lecture. On the Moodle learning platform, 2-4 self-assessment exercises are provided for each lecture, which can be answered from the lecture and the recommended reading, and for which a solution and explanations will be displayed if participants have attempted to solve them. In the last session of the semester there will be a written exam with questions based on the exercises from Moodle.

Assessment and permitted materials

At least 50% of the possible points must be achieved in the written examination at the end of the semester. The questions will be similar to the self-assessment exercises from Moodle.

Minimum requirements and assessment criteria

At least 50% of the possible points must be achieved in the written examination at the end of the semester. The following grading scheme applies depending on the points achievable:

90%-100% of the points: grade 1
80% - <90% of the points: grade 2
65% - <80% of the points: grade 3
50% - <65% of the points: grade 4
<50% of points: failed

Examination topics

In the written exam, questions on the topics of the lectures must be answered.

Reading list

Weekly reading recommendations will be announced as a preparation for the next lecture.

Association in the course directory

Last modified: Tu 14.01.2025 11:45