HDS 5304 Machine Learning and Deep Learning
The subject of this course is closely related to machine learning and data science with an emphasis on statistical aspects/views. This course will introduce various statistical/computational techniques for supervised learning and unsupervised learning. Topics to be covered include basic concepts (such as training versus test errors, cross-validation, bias-variance trade-off), penalized/regularized regression, linear discriminant analysis, tree-structured classifiers, neural networks, support vector machines, classifier ensembles (such as bagging and boosting), unsupervised learning (dimension reduction, clustering analysis, network analysis). Practical application of the methods in health science and biomedical research will be emphasized.