See English version En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:

• Build adaptive lab-in-the-loop machine learning systems.
• Explain common methods of extracting features from molecules, including those based on structural similarity and neural networks.
• Explain common featurization methods for molecules, including those based on structural similarity and neural embeddings.
• Account for different types of experimental noise and uncertainty, managing assays that involve sparse count data or skewed error distributions.
• Derive decision rules for selecting candidates, and explain mathematically the tradeoffs between exploration and exploitation.
• Write probabilistic programs to fit models to data.
• Design experiments based on learned models.
• Test and evaluate lab-in-the-loop machine learning systems with stochastic simulations.

See the English version For questions regarding the course content and prerequisites, please contact the teacher Eli Weinstein. 07. september, 2026