AI and Machine Learning for Biomedical Sciences

This course introduces students to the fundamental principles and practical applications of artificial intelligence (AI) and machine learning methods (ML) in the context of nanobiology and biophysics. The course covers key methods such as deep learning, supervised and unsupervised learning, and their applications to protein structure prediction, bioimage analysis, and biological data interpretation. Students will explore the capabilities and limitations of AI tools, address ethical considerations in AI-driven biological research, and gain hands-on experience through computational practicals. The course consists of lectures, coursework, and guided programming assignments.

1. The grade will consist of contribution to the course and project discussions, the evaluation of the assignments/group project + final presentation, assessment of written reflections of scientific literature (journal articles) and determined as a combination of peer grading and the assessment of the instructor(s). A rubric for peer grading will be provided to the students.

2. There is no opportunity to repair an individual assignment grade.

3. The final grade is composed as follows. Each part must be passed with a minimum of 5.5 to receive a passing grade.

• Group project + presentation: (50 %)

• Written reflection/discussion of literature (30%)

• Course + project discussions (20%)

5. Partial grades are only good for the year that they are earned and do not carry over to the next year.

6. Students needing to turn work in late due to personal circumstances must contact the Academic Counsellor prior to turning it in to make arrangements. Other late work might not be graded.