Probability Statistics - The Foundations of Machine Learning - Dependence and Variance of Two Random Variables

The video tutorial explains the concept of visualizing probabilities using X, Y, and Z axes, focusing on fixing Y to understand likelihoods. It covers the transformation from 3D to 2D plots, normalization to satisfy probability axioms, and the independence of X and Y. The tutorial introduces dependence through a covariance matrix and demonstrates how to visualize higher dimensions using a method by Geoffrey Hinton.

What new insight or understanding did you gain from this video?