Judea Pearl's work wasn't about machine learning so much as introducing the idea of using probability to AI reasoning. In the early days uncertainty was handled in AI using ad hoc techniques (e.g. Mycin) and the use of probability was regarded as too complex because the joint probability distribution grows exponentially with the size of the domain. Pearl showed that probability could be used for reasoning in a natural way using conditional independence to simplify the joint, and that belief networks behaved in interesting ways (e.g. "explaining away"). He then described an elegant algorithm for the propagation of probbilities in a simple form of a belief net (trees). This still forms the basis of inference today, most algorithms create a clique tree from multiply connected networks and use message passing. Shacter et.al. later showed that all exact algorithms are a form of this method. I'm a big fan as you can tell.