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EECS 837 Data Mining
Extracting data from data bases to data warehouses. Preprocessing of data: handling incomplete, uncertain, and vague data sets. Discretization methods. Methodology of learning from examples: rules of generalization, control strategies. Typical learning systems: ID3, AQ, C4.5, and LERS. Validation of knowledge. Visualization of knowledge bases. Data mining under uncertainty, using approaches based on probability theory, fuzzy set theory, and rough set theory. Prerequisite: Graduate standing in CS or CoE or consent of instructor.
Master of Science in Physics
http://catalog.ku.edu/liberal-arts-sciences/physics-astronomy/ms-physics/
...3 EECS 738 Machine Learning 3 EECS 739 Parallel Scientific Computing 3 EECS 837 Data...
Doctor of Philosophy in Business
http://catalog.ku.edu/business/phd/
...3 EECS 649 Introduction to Artificial Intelligence 3 EECS 738 Machine Learning 3 EECS 837...