Explore unsupervised machine learning techniques in this 43-minute lecture from the "Machine Learning for Health and Disease" program. Delve into clustering methods, dimensionality reduction, and their practical applications in healthcare and biomedical research. Learn how these powerful tools can uncover hidden patterns in complex datasets, aiding in patient outcome prediction, medical image analysis, and genomic variant interpretation. Gain insights from expert Rahul Siddharthan on bridging the gap between computational modeling and clinical problems, and discover how unsupervised ML can contribute to building comprehensive public health databases and resources.