Although terminology differs, there is considerable overlap between record linkage (data cleaning) methods based on the Fellegi-Sunter model (JASA 1969) and Bayesian networks used in machine learning (Mitchell 1997). Both are based on formal probabilistic model that can be shown to be equivalent in many practical situations (Winkler 2000). When no missing data are present in identifying fields and training data are available, then both can efficiently estimate parameters of interest. When missing data are present, the EM algorithm can be used for parameter estimation in Bayesian Networks when there are training data (Friedman 1997) and in record linkage when there are no training data (unsupervised learning). This talk describes some of the current methods of approximate string comparison for accounting for typographical error between strings, hidden Markov models for adaptive name and address parsing, methods of semi-supervised learning, fast indexing and retrieval methods for comparing records from files with hundreds of millions of records (Yancey and Winkler 2003), and methods of creating information and data structures during linkage processes. The last set of methods has relationship to the probabilistic relational models of Koller and Pfeffer (1998) and the analytic linking methods of Scheuren and Winkler (1993, 1997).