The second part of the course will focus on basic ideas in classification problems including discriminant analysis and support vector machine, and unsupervised learning techniques such as clustering, principal component analysis, independent component analysis and multidimensional scaling. The course will also cover the modern statistics in the "big data" area. The high dimensional problems when p >> n and n >> p will be introduced. In addition, the students will be formed as groups to do data analysis projects on statistical machine learning and present their findings in class. This will prepare them for future careers in industry or academia.
Priority is given to students enrolled in Statistics Specialist or Major programs.
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