Explore a comprehensive lecture on out-of-distribution generalization in machine learning, delivered by Rajesh Ranganath at the Computational Genomics Summer Institute. Delve into key concepts such as nuisance-induced spurious correlations, invariant risk minimization, and distributionally robust neural networks. Examine real-world examples including cow vs. penguin classification, waterbirds vs. landbirds, and pneumonia detection. Investigate techniques like nuisance randomization and uncorrelating representations to address common issues in machine learning models. Analyze the challenges of natural language inference and explore various coupling assumptions. Gain insights into the role of causality in machine learning and the trade-offs between flexibility and estimation costs. Access related research papers for further study on out-of-distribution generalization techniques and their applications in computational genomics.