Lecture

Principal Component Analysis: Theory and Applications

Description

This lecture covers the theory and applications of Principal Component Analysis (PCA), focusing on dimension reduction and eigenvectors. It explains how PCA can be used to find the optimal linear dimension reduction for random variables, as well as the properties of Gaussian vectors.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_sb94btks

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Mathematics

Algebra: Linear algebra

Chemistry

Organic chemistry: Organometallic chemistry

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