Praktikum Workflows für maschinelles Lernen und reproduzierbare Wissenschaft

After successfully completing the module, students will have acquired practical skills in developing and implementing machine learning workflows. They will be able to apply state-of-the-art-tools to utilize machine learning for reproducible science through reproducible, replicable, scalable, and portable workflows, related visualization and writing tools, and techniques relevant to implementing GDPR provisions for sensitive data.

• Executable and tested prototype (50% of total marks)
• Complete documentation, 10-20 pages (20% of total marks)
• Successful intermediate presentation of results, 10 min (10% of total marks)
• Successful final presentation of results, 20 min (20% of total marks)

Depending on the projects, the relevant literature or material will be shared in the first session of the semester.

Bewertungen von Studierenden

Was Studierende über dieses Modul sagen. Die BTU hat damit nichts zu tun — hier schreiben nur Studierende, und jede Bewertung ist die Meinung einer einzelnen Person.