Lectures in course MGT-448: Statistical inference and machine learning

Explains Bayesian Networks factorization and sampling methods using DAGs and Variable Elimination.

Covers the chain rule in graphical models and introduces variable elimination.

Covers the training process of a neural network, including feedforward, cost function, gradient checking, and visualization of hidden layers.

Explores the Chow-Liu Algorithm for structure learning and optimizing distributions through spanning trees and K-L divergence.

Explores structured learning with the Chau-Lieu algorithm and delves into the basics of deep learning and neural networks.

Explains neural networks, supervised learning, and backpropagation for training and improving performance.

Covers variational inference and neural networks for classification tasks.