Curriculum
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.
Mathematics | Linear algebra Analytic geometry Vector calculus Probability theory, statistics & distributions |
Machine Learning (ML) | Unsupervised Learning
Supervised Learning
Deep Learning
|
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 |