Update CS 483 notes

Author: MoenMi

classes/cs483/1-intro.md

@@ -124,6 +124,8 @@ The goal of clustering is to partition the inputs into regions that contain simi
 
 When dealing with high-dimensional data, it is often useful to reduce the dimensionality by projecting it to a lower dimensional subspace that captures the essence of the data.
 
+One approach to this problem is to assume that each observed high-dimensional output $\boldsymbol{x}_n \in \mathbb{R}^D$ was generated by a set of hidden or unobserved low-dimensional **latent factors** $\boldsymbol{z}_n \in \mathbb{R}^K$.
+
 ### 1.3.3 - Self-supervised learning