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University of Michigan School of Information


721 - Data Mining: Methods and Applications

With the explosive growth of information generated from different sources, in a variety of formats, and with various qualities, information analysis has become challenging for researchers in many disciplines. Automatic, robust, and intelligent data mining techniques have become essential tools to handle heterogeneous, noisy, nontraditional, and large-scale data sets. This is a doctoral seminar course of advanced topics in data mining. The course provides an overview of recent research topics in the field of data mining, the state-of-the-art methods to analyze different genres of information, and the applications to many real world problems. The course will highlight the practical applications of data mining instead of the theoretical foundations of machine learning and statistical computing. The course materials will focus on how the information in different real world problems can be represented as particular genres, or formats of data, and how the basic mining tasks of each genre of data can be accomplished using the state-of-the-art techniques. To this end, the course is not only suitable for doctoral students who are doing research in data mining related fields, but also for those who are consumers of data mining techniques in their own disciplines, such as natural language processing, network science, human computer interaction, economics, social computing, sociology, business intelligence, and biomedical informatics, etc.

Credit Hours 3