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The aim of the curriculum is for participants to learn machine learning methods from the ground up,

  • to understand their potential applications in medicine,
  • to evaluate their results,
  • or to apply them themselves in the future.

We offer mathematics and machine learning methods at different levels, depending on the goals of the participants. We also want to show the extent to which machine learning methods are already being used in medical research and in everyday clinical practice.

We believe it is important to educate medical students about the social and ethical implications of applying machine learning to medical data. This further includes the professional handling of medical data (data literacy). 

Participants are supported on their learning journey through exercises and practical sessions.

More details

The table below gives a more detailed overview of the topics covered in each area. Please note that this list is not exhaustive and will be updated as more learning content is created.


Linear algebra

Analytic geometry

Vector calculus

Probability theory, statistics & distributions

Machine Learning (ML)

Unsupervised Learning

  • Clustering
  • k-Means algorithm
  • Dimensionality reduction
  • Principal component analysis

Supervised Learning

  • Classification & regression
  • Logistic regression
  • k-Nearest neighbors algorithm
  • Support vector machines
  • Decision trees

Deep Learning

  • Introduction to neural networks
  • Multilayer neural networks
  • How neural networks learn

Applications of ML to medical data

Medical image reconstruction

Medical image segmentation

Ethical and social implications & data literacy

Doctor - patient - AI: Implications for shared decision-making

Ethical aspects applying machine learning to clinical data

Information security of AI systems

Medical informatics initiative