136031 UE GenAI for Humanists (2024S)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 05.02.2024 08:00 to Tu 27.02.2024 23:59
- Deregistration possible until Su 31.03.2024 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
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
Friday
01.03.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
08.03.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
15.03.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
22.03.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
12.04.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
19.04.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
26.04.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
03.05.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
10.05.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
17.05.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
N
Friday
24.05.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
31.05.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
07.06.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
14.06.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
21.06.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Friday
28.06.
09:45 - 11:15
Seminarraum 4 Hauptgebäude, Tiefparterre Stiege 9 Hof 5
Information
Aims, contents and method of the course
Assessment and permitted materials
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.
Minimum requirements and assessment criteria
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.
Examination topics
There is no examination for the course.
Reading list
The reading and video lists will be published on Moodle
Association in the course directory
DH Skills II; S-DH Cluster I; S-DH Cluster III; S-DH Cluster IV;
Last modified: Su 28.01.2024 00:02
> 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.