Introduction to Machine Learning and Risk Prediction

Harvard HSPH

Introduces machine learning and risk prediction methods with applications to clinical data. The basic concepts of supervised and unsupervised machine learning methods, as well as appropriate applications will be discussed. Topics covered will include:regularized regression techniques: ridge and lasso regressionstree-based algorithms: random forests and boosted trees (XGBoost)deep learning methods: including convolutional neural networksunsupervised methods: t-SNE and UMAPadditional methods as…