People often say that Bayesian learning and maximum likelihood are two approaches used in machine learning. Main difference is that Bayesian learning tries to include the existing knowledge in the model. The results of these to approaches become closer if the prior knowledge of parameters is non informative or if the sample size approaches infinity.
In a way expectation maximization is implementation of maximum likelihood. Examples of common approach using maximum likelihood include k-means clustering and Gaussian mixture model density estimation.
What are some of the common machine learning algorithms based on Bayesian learning?