This course covers a range of statistical and machine learning methods, including classification, clustering, decision trees, random forest, bagging and gradient boosting for trees and linear models, ridge regression, LASSO, generalized additive models, principal component analysis (singular value decomposition), multiple hypothesis testing, sensitivity and specificity analysis, cross-validation, and bootstrapping. Python will be the primary software used, with R and other environments also used at the discretion of the instructor. This course includes a scientific communication component.
This course covers a range of statistical and machine learning methods, including classification, clustering, decision trees, random forest, bagging and gradient boosting for trees and linear models, ridge regression, LASSO, generalized additive models, principal component analysis (singular value decomposition), multiple hypothesis testing, sensitivity and specificity analysis, cross-validation, and bootstrapping. Python will be the primary software used, with R and other environments also used at the discretion of the instructor. This course includes a scientific communication component.
