CS 550 Machine Learning
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Paradigms and issues of machine learning. Introduction to induction, instance and hypothesis spaces, inductive bias, sample complexity, computational complexity. Theory and methodology of induction. A formulation of a general paradigm for inductive inference. Approaches to supervised attribute based induction. Valiant's learning framework. Instance-based learning. Genetic algorithms, genetic-based machine learning, classifier systems. Learning decision trees. Explanation-based learning. Discovery systems. Learning problem solving strategies. Previous knowledge of artificial intelligence is required.
Credit units: 3 ECTS Credit units: 7.5.
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