A method that takes turns guessing hidden data and updating model settings.
EM is like solving a mystery smoothie. You guess the hidden fruits, tweak the recipe, then guess again.
It helps when real labels are missing. You meet it in clustering and filling in missing data.
Unsupervised Learning
EM is often used to estimate hidden groups and settings without true labels.
KL Divergence
EM steps can be seen as slowly reducing a KL gap.
VI
VI can be seen as a wider version of EM.