Understanding Machine Learning: Exactly Solvable Models

This lecture by the instructor delves into understanding machine learning through exactly solvable models, exploring topics such as sample complexity, models in data science and physics, neural network learning, and the Bayes-optimal generalization. The lecture also covers the teacher-student perceptron, the closed-form solutions, and the computational gaps in learning. The discussion extends to the committee machine, the Langevin state evolution, and the hidden manifold model, providing insights into the landscape analysis and the transition recipes in deep learning theory.