• In one class gaussian, we assume that our data came from Multivariate Normal Distribution
• The distribution depends on $\mu$ and $\sum$
  • $\mu$ defines where is the center of the distribution
  • $\sum$ defines what will be the shape of the distribution
• We learn these 2 variables from the sample data
• When we have the learned weight of $\mu$ and $\sum$, then for any new value, $x$ we find the $f(x)$
  • If $f(x)$ is above a threshold, then it is identified as it has come from that class
  • Otherwise anomaly

Mutivariate Normal Distribution