Statistics 597G - ST- Intro/Statistical Learning

Introduction to some statistical regression and classification techniques including logistic regression, nearest neighbor methods, discriminant analysis, kernel smoothing, smoothing spline, local regression, generalized additive models, decision trees, random forests, and support vector machines. Clustering methods such as K-means and hierarchical clustering will be introduced. Finally, there will also topics on resampling based model evaluation methods and regularization based model selection methods.

STATISTC 525 and 535