John Elder, Ph.D.
Find the useful information hidden in your data! This course surveys computer-intensive methods for inductive classification and estimation, drawn from Statistics, Machine Learning, and Data Mining. Dr. Elder will describe the key inner workings of leading algorithms, compare their merits, and (briefly) demonstrate their relative effectiveness on practical applications. We'll first review classical statistical techniques, both linear and nonparametric, then outline the ways in which these basic tools are modified and combined into powerful modern methods. The course emphasizes practical advice and focuses on the essential techniques of Resampling, Visualization, and Ensembles. Actual scientific and business examples will illustrate proven techniques employed by expert analysts. Along the way, major relative strengths and distinctive properties of the leading commercial software products for Data Mining will be discussed.
John F. Elder IV, Ph.D. heads a top data mining consulting team, based in Charlottesville, Virginia, and Washington DC. Founded in 1995, Elder Research, Inc. focuses on commercial, investment, and security applications of advanced analytics including stock selection, text mining, social networks, image recognition, biometrics, process optimization, drug efficacy, credit scoring, and fraud detection. John holds a BS and MEE in Electrical Engineering from Rice University, and a PhD in Systems Engineering from the University of Virginia, where he’s an Adjunct Professor teaching Optimization or Data Mining. Prior to 18 years leading ERI, he spent 5 years in aerospace consulting, 4 heading research at an investment management firm, and 2 in Rice's Computational & Applied Mathematics department.
Dr. Elder has authored innovative data mining tools, is a frequent keynote speaker, and was Chair of the 2009 Knowledge Discovery & Data Mining conference in Paris. He was honored to serve five years on a panel appointed by the President to guide technology for national security. He has co-authored award-winning books on practical data mining (2009) and ensemble modeling (2010). John is grateful to be a follower of Christ and the father of 5.
Those from industry and academia who work with data and wish to understand recent developments in pattern discovery, data mining, and inductive modeling. At the conclusion of this course, one should be able to discern the basic strengths of competing methods and select the appropriate tools for one's applications. Participants should have prior working experience with computers and interest in applied statistical techniques. (It helps, as well, to have a motivating application you wish to solve.)
|I. Pattern Discovery: An Overview
||VI. Text Mining
Each of the major topics discussed could comprise a semester-long course if presented in full detail! What this (intensive) short course provides is a broad overview of the highlights, drawing connections between major developments in the diverse fields that contribute to Predictive Analytics, including cutting-edge ways to mine text and graphical networks. Previous participants have found this "big picture" to be very useful for identifying techniques to use immediately, as well as approaches worthy of further exploration, for research or practical problem-solving.