Support Vector Machines in R (a course by Lutz Hamel)

Support vector machines (SVM’s) are the “big iron” of the data mining world, especially suited for extreme data intensive tasks like image classification, biosequence processing, handwriting recognition, etc. Dr. Lutz Hamel, author of “Knowledge Discovery with Support Vector Machines”, presents his online course “Introduction to Support Vector Machines In R” November 18 – December 16. “Support Vector Machines in R” teaches you what is going on “under the hood” when you use SVM’s. After completing this course, you will be able to interpret the performance of SVM models, choose model parameters well during the model evaluation and selection cycle, know how linear, polynomial, and Gaussian kernels differ, and know how to tune their parameters. In addition, you will gain a deep understanding of how the cost constant “C” affects the quality of your models. The course is based on the R statistical computing environment. However, the knowledge gained here is easily transferred to other knowledge discovery environments. Dr. Lutz Hamel teaches at the University of Rhode Island and founded the machine learning and data mining group there. Prior to his academic post, Dr. Hamel was Director of Software Development at Thinking Machine Corporation, and Vice President of R&D for Bluestreak, where he oversaw the development of advanced technologies for online ad delivery and optimization, and directed the building of a next generation data warehouse-driven system for campaign analysis and design tools. Participants can ask questions and exchange comments with Dr. Hamel via a private discussion board throughout the course. Registration and details: The course takes place online at in a series of 4 weekly lessons and assignments, and requires about 15 hours/week. Participate at your own convenience; there are no set times when you are required to be online. Other upcoming courses:
  • Oct 28: Interactive Data Visualization
  • Nov 4: Cluster Analysis
  • Nov 4: Financial Risk Modeling
  • Nov 11: Spatial Statistics with Geographic Information Systems
  • Nov 18: Bayesian Regression Modeling via MCMC Techniques
  • Nov 18: Introduction to Support Vector Machines in R (listed above)

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