Applied Statistical Machine Learning.

Catalog description

This course provides several fundamental concepts and methods in statistical machine learning and big data analysis: divide and conquer, parallel computing in R, linear method for regression, lasso, linear method for classification, logistic regression, KNN, model selection and assessment, regression tree, classification tree, bagging, random forest, boosting, support vector machine, neural networks, K-means clustering, principal components analysis. We use R to implement all the methods in this course. NOTE: It cannot be taken as credit towards M.S. in Applied Statistics or the Ph.D. degree or the Graduate Certificate in Statistics, but may be taken as credit for the Big Data Statistical Analysis Graduate Certificate. This course is also part of the M.S. degree program in Data Science. Cross-listed with DATA 712 .