INF 385T - Special Topics in Information Science: Applied Data Mining
Graduate standing. Additional prerequisites may vary with the topic.
Study of the properties and behavior of information. Technology for information processing and management.
Three lecture hours a week for one semester.
May be repeated for credit when the topics vary.
This course will provide an introduction to data mining methods and applications. Data mining is now ubiquitouss, informing decisions concerning everything from supermarkets to political campaigns and health-care. This has given rise to increased hype around 'big data' and data mining. In this course we will first take a broad survey of data mining, its uses and its limitations. We will also discuss the sometimes thorny ethics of data mining. We will then motivate and introduce specific techniques by way of concrete applications: for this we will take a hands-on approach using the Weka software. Students will thus gain practical familiarity with data mining approaches.
All interested and motivated students are welcome in the class: no programming experience will be assumed. An effort will be made to both cater to students interested in the technical aspects of data mining as well as those interested in gaining broad exposure to and familiarity with data mining systems.
Applied Data Mining