• Students are able to demonstrate ML and basic DL techniques to apply AI models to real world tasks and problems in the medical context.
• Students are able to:

• Identify ML models
• Identify DL models
• Identify data requirements
• Explicate problems and tasks
• Preprocess data
• Train and evaluate ML techniques
• Present results

Completion of the modules "Statistics", "Introduction to Machine Learning"

The area of data analytics and neural networks is a very dynamic area, and this description is exemplary, to be applied as an example and includes the state of knowledge of 2022:

• Data requirements
• Application of supervised and unsupervised techniques to data
• Neural Network fundamentals
• Data storytelling

• Lecture script as provided in the module (required)
• S. Papp et al: The Handbook of Data Science and AI, 2022
• F. Chollet: Deep Learning with Python. Manning, 2nd ed. 2020
• P. Johannesson & E. Perjons: An Introduction to Design Science, 2014

teaching & discussion, demonstration, practical examples, groupwork in teams, homework

integrated module examination
immanent examination character: presentation, assignment reports, exam (oral/written)