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Last Updated: 2026-06-03 00:14:08
Abstract
This module discusses latest trends in AI and how to integrate them in the process of designing, implementing, and maintaining IT technologies. We study large language models, recommendation systems, and reinforcement learning. Participants apply this knowledge in a project where they assist a small retailer store to integrate AI in their business.
Objective
How AI works from a systemic and constructive perspective. How (large) language models work, how they reason, and how they are affected by the choice of prompt. How LLM-based agents work. How recommendation systems work. How to do MLOps. How reinforcement learning works.
Content
The contents span three topics. Large language models: How language models represent probability distributions over text. Reasoning in LLMs. Prompt engineering. Agentic AI. Recommendation systems: Bandits. Matrix factorization. Model training, maintenance, and deployment. Governance of ML models. Reinforcement learning: Decision processes, iterative learning, autonomous systems and agents.
General Information
- Language
- English
- Levels
- WBZ
- Frequency
- Semesterly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | AI in Industry |
|
32 h semesterly |