PCA: Directions of Largest Variance

This lecture covers the concept of Principal Component Analysis (PCA) and its application in finding the directions of largest variance in a dataset. It explains how PCA can be used to reduce the dimensionality of data and visualize high-dimensional datasets. The lecture also discusses the process of decorrelating data using PCA, exploring the wine dataset, and the limitations of PCA in dimension reduction.