Explore a comprehensive lecture on incorporating domain knowledge in deep generative models for weakly supervised clustering, with applications to survival data. Delve into Laura Manduchi's research on integrating pairwise constraints and survival data into clustering algorithms, enabling exploratory analysis of complex biomedical datasets. Learn about the challenges of unsupervised clustering and the importance of guiding algorithms towards desirable configurations using prior information. Discover how leveraging side information in biomedical datasets can lead to medically meaningful findings. Examine topics such as weekly supervised clustering, survival clustering examples, synthetic and real-world experiments, nonparametric priors, and conditional Gaussian mixtures. Gain insights into the application of these techniques to infant echocardiograms and other clinical variables.