A four-day summer school equipping life scientists with the practical skills to apply machine learning to their own data.
This four-day summer school equips life scientists with the practical skills to apply machine learning to their own data. Mornings cover the methodological foundations — supervised and unsupervised learning, model evaluation, and the pitfalls specific to biological datasets. Afternoons are spent in hands-on coding sessions working with realistic biological problems.
The programme includes a one-day mini-symposium on Wednesday in which researchers present current applications of machine learning in biological and biomedical research, providing context for how the methods covered translate into ongoing science.
PhD students, postdoctoral researchers, and scientific staff in the life sciences who want to move beyond using machine learning as a black box.
Prior experience with R is helpful but not required; participants without programming background should expect to invest additional effort during the practical sessions.
The ability to read ML methods sections critically and discuss them with collaborators.
You will train, validate, and interpret models on biological data during the course.
Recognising when ML is the right tool, which method fits the question, and how to avoid the most common failure modes — data leakage, overfitting, batch effects.
Four days of close interaction with instructors, invited speakers, and peers from across Swiss life-science institutions.
The summer school takes place in Room 304 of the Main Building (Hauptgebäude) of the University of Bern, centrally located in the heart of the city.
Bern is easily reachable by train from all major Swiss cities. The main building is a 10-minute walk from Bern central station.
University of Bern — Main Building
Hochschulstrasse 4
3012 Bern, Switzerland
Room 304 (3rd floor)