I am confused in understanding the Maximum Likelihood as a classifier. I know what is Bayesian network and I know that ML is used for estimating the parameters of models. Also, I read that there are two methods to learn the parameters of a Bayesian network: MLE and Bayesian estimator. The question which confused me is that:
Can we use ML as a classifier? For example, can we use ML to model the user' behaviors to identify the activity of them? If yes, How? What is the likelihood function that should be optimized? Should I suppose a normal distribution of users and optimize it?
If ML can be used as a classifier, what is the difference between ML and BN to classify activities? what is the advantages and disadvantages of each model?