HCI meets data mining: principles and tools for big data analytics

Abstract

This two-part course takes a practical approach to introduce you to the principles, tools and pitfalls in big data analytics. Part 1: A non-technical introduction illustrating where HCI and data mining as fields of research and practice can benefit from each other with illustrative case studies, followed by a review of tools for analyzing datasets from small to huge. Part 2: A more technical discussion of how to "do it right", such as: How to choose a "big data" platform for your work (or do you need one at all)? How to find an algorithm that is right for your data? How to evaluate your approach appropriately? And more... Audience: HCI researchers, practitioners, and students. No prior knowledge of data mining or machine learning is required. Teaching Methods: Lecture and videos. Instructor Background: Prof. Polo Chau has been working at the intersection of HCI and data mining for over 9 years, to create scalable, interactive tools for big data analytics. Now a professor at Georgia Tech's College of Computing, Polo holds a Ph.D. in Machine Learning and a Masters in HCI, both from Carnegie Mellon. His thesis on bridging HCI and data mining for making sense of large network data won received Carnegie Mellon's Distinguished Computer Science Dissertation Award, Honorable Mention. He teaches the "Data and Visual Analytics" course at Georgia Tech. Polo is the only two-time Symantec fellow. He contributes to the PEGASUS peta-scale graph mining that won an Open Source Software World Challenge Silver Award. Polo's NetProbe auction fraud detection research appeared on The Wall Street Journal, CNN, TV and radio. His Polonium malware detection technology protects 120 million people worldwide.