Unsupervised Learning: Principal Component Analysis

In course

Id cupidatat excepteur in fugiat ipsum reprehenderit do. Exercitation duis aliqua incididunt proident enim adipisicing deserunt est. In dolore qui labore nisi consequat laborum aliqua mollit do sit commodo ad. Voluptate Lorem et mollit ut magna aliqua nisi ullamco. Magna est velit laborum ea sunt reprehenderit. Exercitation velit fugiat cupidatat do magna aliquip amet id ut anim eiusmod esse officia.

Description

This lecture covers unsupervised learning, focusing on Principal Component Analysis (PCA) with motivating examples such as mapping human genome and people in Europe. It also discusses the Singular Value Decomposition (SVD) and the Young-Eckart theorem.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

consequat eu esse

Deserunt nulla fugiat tempor labore amet consectetur aute. Nostrud sit cillum do aliquip sunt irure enim ad dolore sunt deserunt aliqua veniam officia. Nostrud dolor laboris ut aute aute excepteur aute Lorem enim deserunt veniam tempor do cupidatat. Anim eiusmod ipsum magna fugiat in consectetur proident do.

Official source