Curriculum

Actually, you are supposed to see a video here. To display this content (source: www.xyz.de), please click the "Accept" button below. Please note that by viewing the video, data might be transmitted to third parties or cookies may be stored.

For further information see our privacy policy.

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 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

External content