Lecture

Gaussian Mixture Model: EM final form

In course

Id id do do minim duis. Proident in magna mollit dolor proident fugiat. Amet amet laborum occaecat ipsum tempor magna enim nostrud.

Description

This lecture covers the calculations for the E-step and the final results for the M-step in the context of the Gaussian Mixture Model. The instructor explains the expected complete likelihood, the probability distributions, and the updates for the parameters. The pseudocode of the EM algorithm is also presented, providing a summary of the main updates. By the end of the lecture, viewers will have a clear understanding of the algorithm and its application in clustering.

Official source

https://mediaspace.epfl.ch/media/0_4js7rdfl

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.

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Information engineering

Machine learning: Unsupervised learning

Related lectures (49)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.