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This course will cover the foundations of business data mining. It will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. Topics include: Knowledge representation and different types of data; Techniques for data pre-processing, cleaning, reduction, transformation, and visualization; Methods for Classification, Clustering, and Association Rules, including Decision Trees, Rules, Naive Bayes, k Nearest Neighbor, Neural Networks, Support Vector Machines (SVM), One R, Regression, A-Priori, K-means, and hierarchical and density-based clustering; Performance evaluation of data mining algorithms using metrics like precision, recall, f-measure, and ROC curves. This course uses real world data sets and Weka software which is a collection of machine learning algorithms for data mining tasks that can be downloaded for free. Prerequisite: BSTAT 5325 or equivalent. May be taken concurrently.

Business Administration - Graduate Programs

...Management of Information Technologies (INSY 5375) International Marketing...MANA 5320) Entrepreneurship* (MANA 5339) Global Supply Chain...