Machine Learning with Small Data Part 1

This course addresses the challenge of machine learning (ML) in the context of small datasets, a significant issue due to ML's increasing data demands. Despite ML's success in various fields, many areas can't provide large labeled datasets because of costs, privacy, or security laws. As big data becomes standard, efficiently learning from smaller datasets is crucial. This course, ideal for graduate students with some ML experience, focuses on modern deep learning techniques for small data applications relevant in healthcare, military, and various industry sectors. Prerequisites include ML familiarity and Python proficiency. Deep learning experience is not necessary but beneficial.