Biostatistics 750 - Applied Statistical Learning

The goal of this course is introduce various statistical learning methods that are widely used in medical and public health research. The topics in this course include penalized linear regression, dimension reduction regression, logistic regression, discriminant analysis, support vector machines, tree-based methods, principal component analysis and clustering. The resampling procedures including cross-validation, bootstrapping and permutation tests, which are important in model evaluation and inference, will be introduced as well. The course emphasizes on helping students understand the concepts and ideas of some modern statistical learning methods and apply these methods to research medical and public health studies. Implementation of different methods with R software will be introduced whenever appropriate

Open to Graduate students only. BIOSTATS 540 & 640 UG require instructor permission