Machine Learning with Limited Data

4.0 credits

This course will introduce state-of-the-art techniques for data-efficient Machine Learning, focusing on three core areas: (1) transfer learning and domain adaptation, (2) learning with limited supervision (semi-supervised, self-supervised, and active learning), and (3) few-shot learning and data augmentation strategies. Students will engage with recent research literature and gain hands-on experience applying these methods across diverse domains. Assessment is based on a substantial term project where students implement and evaluate data-efficient techniques on a problem of their choosing, along with smaller assignments throughout the semester.