Universität Wien

136031 UE GenAI for Humanists (2024S)

Prüfungsimmanente Lehrveranstaltung

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

Classes will take place in person, in the University, with some classes online, via zoom.
Attendance to both in person and online classes is recorded and count towards the evaluation

Freitag 01.03. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 08.03. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 15.03. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 22.03. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 12.04. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 19.04. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 26.04. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 03.05. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 10.05. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 17.05. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 24.05. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 31.05. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 14.06. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 21.06. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Freitag 28.06. 09:45 - 11:15 Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The aims of the course are:
> Understanding Generative AI Concepts:
-- Provide a basic understanding of the Neural Networks Architecture underlying Sequence2Sequence and Generative Models
-- Provide a comprehensive understanding of generative AI concepts and tools, including generative models for text, images and speech data.
> Practical Skills Development:
-- Learn basics of Prompt Engineering
-- Use popular Python LLM frameworks like LangChain and LLamaIndex
-- Use Generative AI models using Open Source Models from Hugging Face and proprietary APIs like OpenAI ChatGPT.

> Applications and Use Cases:
-- Explore real-world applications and use cases of generative AI across different industries, such as image synthesis, text generation, and sound processing.
> Ethical Considerations:
-- Discuss ethical considerations related to generative AI, including issues like bias, fairness, and responsible AI deployment.

Content will cover, but will not be limited to: Social, Technical and Practical aspects of GenAI; LLMs and other Generative Models; LLMs and GenAI for Humanities; Prompt Engineering Techniques; Python Frameworks; Few-shots learning; Fine Tuning models; Using APIs; working with Images; working with speech data; working with tabular data, Large Action Models - Agents.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Evaluation will be mainly through individual and group hands-on assignments and a final capstone project. Participation in the class discussion and activities and an equivalent amount of extra-class dedication to the materials shall be enough to succeed in the course.

Mindestanforderungen und Beurteilungsmaßstab

Attendance is required; regular participation is the key to completing the course; all students must provide their computing environment; homework assignments must be submitted on time (some can be completed later as a part of the final project, but this must be discussed with the instructor whenever the issue arises); the final project must be submitted on time.

Prüfungsstoff

There is no examination for the course.

Literatur

The reading and video lists will be published on Moodle

Zuordnung im Vorlesungsverzeichnis

DH Skills II; S-DH Cluster I; S-DH Cluster III; S-DH Cluster IV;

Letzte Änderung: So 28.01.2024 00:02