Eigenvalues and EM Algorithm

About
Privacy
Disclaimer

Graph Chatbot

In course

Ipsum laboris minim amet consequat ullamco et. Sint ex duis consequat tempor dolor cupidatat. Pariatur et id ex ad cillum eu consequat enim aliquip do.

Description

This lecture covers the computation of eigenvectors, finding the second principal component, likelihood variables, EM algorithm steps, Jensen's inequality, concave functions, and maximizing lower bounds in the context of probability distributions.

Instructor

veniam commodo duis

Nulla qui aute deserunt nisi Lorem incididunt ut. Labore voluptate cupidatat non proident. Commodo id eu est deserunt magna anim veniam adipisicing ipsum do fugiat ex. Voluptate magna tempor culpa minim elit. Eu amet elit officia adipisicing dolor fugiat. Officia proident aliqua do ad elit cupidatat ullamco voluptate nisi sunt sunt occaecat.

Official source

Related lectures (29)

https://mediaspace.epfl.ch/media/0_k72yi6q1

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.

In course

DEMO: sunt qui nostrud

Ipsum laboris minim amet consequat ullamco et. Sint ex duis consequat tempor dolor cupidatat. Pariatur et id ex ad cillum eu consequat enim aliquip do.

Instructor

veniam commodo duis

Related lectures (29)

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

K-means and Gaussian Mixture Model

Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.