Statistics 540 - Intro/Statistical Learning

Introduction to some modern 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, support vector machines and deep learning. 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. The course emphasizes the mathematics behinds these methods sufficient to understand the differences among the methods as well as the practical implementation of them.

STATISTC 516 or 490S