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Artificial Intelligence training begins with a basic understanding of machine learning, where students delve into concepts such as supervised, unsupervised, and reinforcement learning to understand the core principles that underpin the ability of AI systems to learn from data. Most of our training consists of classic lecture-style training and additional hands-on sessions. In these practical sessions, we use Python programming, with a focus on libraries such as PyTorch that facilitate the development of deep learning models. Students learn to implement and/or use AI architectures such as convolutional neural networks, transformers and random forest classifiers, enabling them to tackle complex tasks such as image recognition and natural language processing. By solving real-world AI projects and applications, our students gain hands-on experience in problem-solving and model development. This hands-on experience helps them to bridge the gap between theory and application, fostering careers in AI research and application development.

Another integral part of Artificial Intelligence training is exploring the ethical considerations and societal implications of AI technologies. Trainees examine issues such as bias in algorithms, the role of AI in decision-making, and the potential consequences of widespread automation, fostering a responsible and thoughtful approach to AI development.

Related Living Lab lectures

• Ethics and Moral Code in AI
• Ethics and AI: A Complicated Relationship?
• Privacy-Preserving Data Processing and Machine Learning
• Security Research: Intrusion Detection
• Artificial Intelligence in Process Mining
• asanAI: Introduction

Related online materials

• Prompt engineering tutorial
• Generative Artificial Intelligence Notebooks
• Large Language Models, an Introduction for Life-Scientists