Document Type
Article
Publication Date
2015
Abstract
Machine Learning via Artificial Neural Networks (ANNs) is often introduced in a one-semester course on Artificial Intelligence. Baseball’s annual Hall of Fame election provides a simple, tractable, data-rich domain for learning how to use ANNs for predictive analytics. We describe how we use the Fast Artificial Neural Network (FANN) toolkit for a course assignment that predicts which players are likely to be elected to Baseball’s Hall of Fame.
Recommended Citation
Hansen, David, "Introducing Machine Learning via Baseball's Hall of Fame" (2015). Faculty Publications - Department of Electrical Engineering and Computer Science. 3.
https://digitalcommons.georgefox.edu/eecs_fac/3
Comments
© CCSC, (2015). This is the author's version of the work. It is posted here by permission of CCSC for your personal use. Not for redistribution. The definitive version was published in The Journal of Computing Sciences in Colleges, 30, 4, April 2015, http://dl.acm.org/.