Advanced Statistical Learning

3 credits · Graduate credit

This course is an advanced introduction to modern statistical learning. Topics include dimension reduction techniques, including principal component analysis, factor analysis, multidimensional scaling and manifold learning; conceptual framework of classification including cost functions, Bayes classifiers, overfitting and generalization; specific classification methods including logistic regression, naive Bayes, discriminant analysis, support vector machines, kernel-based methods, generalized additive models, tree-based methods, boosting, neural networks; clustering methods including K-means, model-based clustering algorithms, mixture models, latent variable models, hierarchical models; and algorithms such as the EM algorithm, Gibbs sampling, and variational inference methods. Additional topics that may be covered include categorical data analysis, graphical models, and deep learning.