Lecture

Expectation Maximization and Clustering

Description

This lecture covers the Expectation Maximization algorithm and clustering techniques, focusing on topics such as Gibbs Sampling, detailed balance, posterior inference, and simulated annealing. The slides discuss the process of updating configurations, balancing probabilities, and the idea of clustering data points into groups based on similarity.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.