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Machine Learning Semester Course
The purpose of the course is to introduce a class of learning methods having more to do with (what I will call) predicate descriptions of the training examples. Neural nets are not covered because this is another course in this series and genetic algorithms are not covered (extensively) because discussion of this should be under optimization and search techniques. ID3 is used as the introduction to learning methods in general and specific problems effecting this and other learning methods (i.e. missing data, pruning) will be discussed within the framework of this method. Other methods will also be introduced: Mikalskis's AQ and Conceptual Clustering and the class of incremental concept formation algorithms, EPAM, UNIMEM and COBWEB (and maybe CLASSIT).
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