Lecture 21 – Principal Components Analysis

Presented by Anthony D. Joseph

Content by Josh Hug, John DeNero, Sam Lau, and Suraj Rampure

• slides
• video playlist
• code
• Summer 2020 PCA notes
• PCA tutorial

A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.

	Video	Quick Check
22.1 Dimensionality. Visualizing high-dimensional data.		22.1
22.2 More visualizations of high-dimensional data.		22.2
22.3 Matrix decomposition, redundancy, and rank. Introduction to the singular value decomposition (SVD).		22.3
22.4 The theory behind the singular value decomposition. Orthogonality and orthonormality.		22.4
22.5 Definition and computation of principal components. Geometric interpretation of principal components and low rank approximations. Data centering.		22.5
22.6 Interpretation of singular values. The relationship between singular values and variance. Analyzing scree plots.		22.6
22.7 Introduction to principal Component analysis (PCA). PCA for exploratory data analysis.		22.7