Learn about advanced machine learning concepts in this university lecture covering local version inference, alternating updating methods, and customizing updates within graphical models. Explore mean field version updates, general case scenarios, and inference frameworks while diving into word distribution analysis. Master the theoretical foundations and practical applications of these statistical learning techniques through comprehensive explanations and examples presented in this 81-minute academic session.
Probabilistic Graphical Models and Mean Field Inference - Lecture 20
Overview
Syllabus
Introduction
Local Version Inference
Alternating Updating
Customizing Updating
Graphic Model
Mean Field Version Update
General Case
Inference Framework
Word Distribution
Taught by
UofU Data Science
