Explore a 32-minute conference talk on ProtoDash, a fast and interpretable prototype selection method for machine learning. Learn about extracting compact representations of large datasets, understanding prototypes and criticisms, and their applications in causal reasoning. Dive into the mathematical foundations, including set function maximization and submodularity properties. Compare ProtoDash and ProtoGreedy algorithms, examining their performance in classification accuracy, sparsity, and computation time. Discover how to visualize selected prototypes and criticisms, and explore real-world applications such as finding relationships between datasets and improving prediction accuracy through training data selection. Gain insights into theoretical guarantees and engage with the Q&A session to deepen your understanding of this innovative approach to interpretable machine learning.