Explore a lecture on interpretable machine learning through program synthesis presented by Osbert Bastani at IPAM's Explainable AI for the Sciences workshop. Delve into novel approaches for creating custom model families using domain-specific programming languages, moving beyond traditional fixed model families. Discover applications in learning interpretable control policies and RNA splice prediction. Examine topics such as parallel parking control, video trajectory queries, deep reinforcement learning, and multi-agent reinforcement learning. Investigate the Viper algorithm, state machine policies, and neurosymbolic transformers. Gain insights into programmatic attention rules, sparse communication structures, and modular networks for RNA splicing in this comprehensive exploration of cutting-edge interpretable machine learning techniques.