Structured Learning: Chau-Lieu Algorithm

This lecture delves into structured learning, focusing on the Chau-Lieu algorithm, which efficiently learns a tree approximation to a joint distribution. The algorithm aims to reduce storage requirements and simplify the learning process for high-dimensional structures. By utilizing mutual information estimates and a max weight spanning tree, the algorithm efficiently approximates the true joint distribution. Additionally, the lecture covers the basics of deep learning, explaining the concept of neural networks, layers, activation functions, and the training process through gradient descent. The instructor provides insights into the complexities of training neural networks, emphasizing the importance of empirical risk minimization and regularization techniques.