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Machine Learning PDF Print E-mail
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Sunday, 27 November 2005
Machine Learning 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). This is all the course materials and slides (to date) for the winter semester 94 lecture on Machine Learning. 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). Apart from the theoretical introduction to machine learning, experiments in parameter specification and analysis (using the ANALYSIS system) will be performed. The course is itself an experiment in "Learn by Doing".
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