Predictive Analytics II

1 Unit · Graduate

This course covers nonparametric modeling of complex, nonlinear predictive relationships between variables. Covered supervised learning methods include neural networks, trees, nearest neighbors, local kernel weighting, boosted trees, random forests, support vector machines, and naive Bayes. Emphasis is on practical implementation of predictive modeling, as well as theoretical concepts for building a deeper understanding of the methods.