I am a fan of MOOCs (Massive Open Online Courses). These courses allow people everywhere (well, at least those with access to the Internet) to have access to courses taught by the top people in the field. For example, Peter Norvig, head of research at Google and the author of the top textbook on artificial intelligence, and Sebastian Thrun, a research professor at Stanford who lead the development of a robotic vehicle that won the DARPA challenge race to drive a 150 mile mountainous course, taught an free online course on artificial intelligence. 160,000 people in 190 countries enrolled in the course. This is a phenomenal shift in education. Students no longer need to be accepted to elite universities to have access to the highest quality instruction. I’ve used MOOCs in several of my courses ranging from introduction to computer science courses to upper level courses.
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Teaching
Textbooks for data mining
I finally made a decision regarding what textbook to use for a data mining course I will be teaching in the spring. One challenge was that the course is cross-listed in a variety of departments: computer science, business, and information technology and, as a result, the students taking the class will have a diversity of backgrounds–some strong in statistics, others in programming. My original plan was not to have people do programming at all and have them just use Weka, a free, data mining tool. I was considering 2 textbooks: Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar; and Data Mining: Practical Machine Learning Tools and Techniques, by Ian Witten and Eibe Frank. Continue reading