# Can maximum likelihood be used as a classifier?

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 are the following.

1. Can we use ML as a classifier? For example, can we use ML to model the user's 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?

2. If ML can be used as a classifier, what is the difference between ML and BN to classify activities? What are the advantages and disadvantages of each model?

• Hi Atena and welcome to this community. Please, ask just one question per post!
– nbro
Sep 13 '19 at 22:09