Modul


Allgemeine Informationen
Generative AI
Generative AI
MADS-EMGAI
Prof. Dr. Prange, Michael (michael.prange@fh-kiel.de)
Brede, Max (max.brede@fh-kiel.de)
Klick, Alwin (alwin.klick@fh-kiel.de)
Sommersemester 2025
1 Semester
In der Regel jedes Semester
Englisch
Studiengänge und Art des Moduls (gemäß Prüfungsordnung)

Kompetenzen / Lernergebnisse
Kompetenzbereiche: Wissen und Verstehen; Einsatz, Anwendung und Erzeugung von Wissen; Kommunikation und Kooperation; Wissenschaftliches Selbstverständnis/Professionalität.
Students
- know the fundamentals of generative AI systems.
- know various modern applications of generative AI systems.
- know the theoretical foundations and practical applications of generative AI systems.
Students
- are able to explain and apply various open-source language models.
- are able to implement and utilize agent systems and their functionalities.
- are able to understand and use embeddings and vector stores for semantic search and recommendations.
- are able to explain and practically apply different methods for image generation.
- are able to fine-tune large language models (LLMs) and diffusion models for specific tasks.
Students
- are able to successfully organize teamwork for generative AI projects.
- are able to report and present team solutions for practical project tasks.
- are able to interpret and communicate the approaches in technical and functional terms.
Students
- are able to work professionally in the field of generative AI systems.
- are able to give and accept professional feedback to different topics of generative AI systems.
- are able to select relevant scientific literature about generative AI systems.
Angaben zum Inhalt
Open Source Language Models
- Overview of model lists
- Ollama
- Generation of synthetic text as training sets

Agent Systems
- Llamaindex, LangChain & Haystack
- Function calling
- Data analysis

Embeddings and Vector Stores
- Semantic Search
- Retrieval-augmented generation
- Recommendations

AI Image Generators
- Generative Adversarial Networks (GANs)
- Variational Autoencoders / Diffusion Models
- Generative approaches for image dataset augmentation

Fine-Tuning of LLMs and Diffusion Models
- Examples: LoRA, QLoRA, MoRA
Presentation slides
Lehrformen der Lehrveranstaltungen
Arbeitsaufwand
4 SWS
5,0 Leistungspunkte
48 Stunden
102 Stunden
Modulprüfung
Sonstiges
Basic knowledge about Deep Learning and Natural Language Processing.
Basic practical experience in Python programming.