Machine Learning and Biostatistics in Healthcare

3 credits · Graduate

Machine learning (ML) algorithms are widely applied in our daily lives. The overarching goal of this course is to provide students with an overview and hands-on experiences of popular machine learning methods and biostatistical models adopted in the healthcare system and medical research. The topics of the class include supervised learning methods (GLM, SVM, metric learning, tree-based approaches, and shrinkage based approaches), semi-supervised learning (transduction learning, inductive learning), deep learning and neural networks, adaptive experiments, reinforcement learning and multi arm bandit algorithm, causal inference and resampling based statistical inference. The course will also cover the applications of these methods.