Introduction to Generative AI and Agentic AI (CE-AI)
Introduction to Generative AI and Agentic AI (CE-AI)
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Information Systems
Project Description
The course treats Generative AI (GenAI) and Agentic AI in a holistic manner. It discusses technical aspects relevant to application and implementation such as RAG, tool integration for agents. It also discusses risks and opportunities including a number of use-cases:
- GenAI and Agentic AI Project Lifecycle: From idea to evaluation and monitoring
- Adapting and enhancing foundation models, e.g., using retrieval, tools, fine-tuning and prompting
- Opportunities and risks of AI agents and GenAI
- Use-cases in various industries and for various tasks for AI Agents and GenAI
- Evaluation of GenAI & Agents
Teaching Method
The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
Learning Objectives
After successful completion of the course, students will
Professional competence
Methodological competence
Social competence
Personal competence
Technological competence
Professional competence
- understand and effectively apply Generative AI technologies within various business contexts, aligning them with strategic goals.
- assess data needs and feasibility of Generative AI projects
Methodological competence
- master a range of methodologies for developing, adapting, and implementing Generative AI models
Social competence
- develop skills in managing the interface between human workers and AI systems, fostering productive collaboration and ethical interactions.
Personal competence
- enhance problem-solving skills and ethical decision-making in scenarios involving advanced AI technologies.
Technological competence
- Understand techniques to adopt Generative AI such as RAG, fine-tuning as well as evaluation methods
Assessment Methods
Written exam (60min)