Lecture
This lecture discusses optimal errors and phase transitions in high dimensional generalized linear models. It covers the concept of generalized linear models, the significance of high dimensionality, and phase diagrams for estimation problems. The instructor explains the symmetric door perceptron model and its relation to achieving the minimum information-theoretic recovery threshold. Additionally, the lecture explores the link between these concepts and modern machine learning algorithms, emphasizing comparisons with GAMP and IT computations.