Explore a 40-minute lecture on tractable learning in structured probability spaces presented by Adnan Darwiche from UCLA at the Simons Institute. Delve into topics such as representation learning, learning with constraints, and structured probability spaces. Examine examples from video, language, and deep learning domains. Investigate Boolean constraints, combinatorial objects like rankings, and their encoding in logic. Learn about structured spaces for paths, logical circuits, and properties like decomposability and determinism. Discover Sentential Decision Diagrams (SDD) and their probabilistic counterpart, PSDD. Understand how these structures enable tractable logical and probabilistic inference. Explore learning algorithms, preference distributions, and structured datasets. Gain insights into learning from incomplete data and structured queries. Enhance your understanding of advanced machine learning concepts and their applications in various domains.