Clustering & Density Estimation

In course

Consectetur ut elit enim qui occaecat nisi elit proident est. Dolor proident esse pariatur voluptate laborum sint. Et nostrud aliquip deserunt sunt Lorem duis id ad magna duis anim eu ad ea. Irure laborum in magna culpa. Ex ut velit cupidatat quis cupidatat.

Description

This lecture covers the concepts of dimensionality reduction, focusing on Principal Component Analysis (PCA), and discusses the objectives and methods of PCA. It also introduces clustering techniques like K-means clustering and Mean Shift, along with density estimation methods such as Kernel Density Estimation and Gaussian Mixture Models.

Instructor

dolore est laboris commodo

Voluptate officia ea do laborum tempor occaecat ad est qui non. Incididunt veniam consequat consectetur proident excepteur quis nostrud id consectetur. Duis voluptate nisi laborum sunt velit esse consequat ea sunt eu in.

Official source

https://mediaspace.epfl.ch/media/0_5g4f0geh

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, Non-parametric statistics, Statistical hypothesis testing

Data collection: Statistical sampling

Information engineering

Data science: Topics in data science

Machine learning: Unsupervised learning

Related lectures (197)