Unsupervised Learning, 7.5 credits

The course aims to introduce both basic and modern concepts in statistical learning without training data (unsupervised learning) with applications in statistical data analysis. Key concepts reviewed include similarity metrics, linear and nonlinear dimension reduction methods, centroid, distribution and density based methods for cluster analysis, visualization of high dimensional data, hierarchical methods and various validation methods.

Further course information will appear soon on this page. Until then, information can be found on the department website.

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