inf530 - Artificial Intelligence (Complete module description)

• Basic knowledge of computer science/business informatics

Learning Objectives
Students are familiar with key concepts, methods, and application areas of Artificial Intelligence. They understand fundamental learning methods such as KNN, K-Means, and neural networks, as well as modern concepts like Transformers, LLMs, and prompting. They are able to implement models in Python, evaluate their performance, and assess their generalization. Students understand how agent-based AI systems work and grasp the basics of reinforcement learning. They confidently apply AI methods to new problems and reflect on their ethical and societal implications.

Subject-Specific Competencies
Students:
- explain key concepts of AI, machine learning, and neural networks,
- distinguish between supervised and unsupervised learning methods,
- describe how Transformers, LLMs, and prompting techniques work,
- identify fundamental principles of reinforcement learning,
- analyze strengths and weaknesses of various AI methods.

Methodological Competencies
Students:
- practically apply AI models in Python (e.g., using scikit-learn, Keras, Hugging Face),
- evaluate models using appropriate metrics (e.g., accuracy, precision),
- develop and test their own prompting strategies,
- assess the transferability of methods to new tasks,
- compare and optimize AI methods for specific applications.

Social Competencies
Students:
- collaborate on practical AI projects,
- critically discuss results within a team,
- present solutions tailored to specific audiences.

Personal Competencies
Students:
- reflect on the use of AI methods in their own projects,
- recognize the ethical implications of current AI systems,
- develop awareness of the opportunities and limitations of generative AI.

- Fundamentals of Artificial Intelligence
- Machine Learning
- Deep Learning
- Transformer Models and Large Language Models (LLMs)
- Prompting and Agent-Based AI
- Reinforcement Learning
- Practical Implementation in Python

- Introduction to Statistical Learning — Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Dive into Deep Learning — Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
- Pattern Recognition and Machine Learning — Christopher M. Bishop
- Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Artificial Intelligence: A Modern Approach — Stuart Russell, Peter Norvig

At the end of the lecture period

Written or oral exam