Course description

The goal of data mining is to find and exploit valuable insights and relationships in large, complex data sets. Massive size and high complexity of data sets has transformed the practice of data mining in the twenty first century. Data mining algorithms have advanced rapidly to address this growth in size and complexity. Applications of data mining include web search, interactions in social networks, finding relationships in large internet-of-things (IOT) sensor networks, and finding interactions between drugs. This course surveys a range of algorithms used for key applications of data mining. The emphasis of the course is on unsupervised learning, semi-supervised learning, and graph algorithms. Scaling and computational efficiency of data mining algorithms is addressed. Lectures and readings introduce core theoretical concepts. Students apply the theory and methods using Python tools in hands-on exercises and projects. For the hands-on component of the course, students use a variety of libraries in the Python language. Examples include Scikit-Learn, Surprise, Neo4J, and NetworkX.

Instructors

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