STAT4051

This is the first semester of the applied statistics and statistical machine learning sequence for majors seeking a BA or BS in statistics or data science, coupled with the course STAT 4052. The course delves into the foundational statistics supporting contemporary machine learning techniques. The emphasis lies on identifying problem types, selecting appropriate analytical methods, accurate result interpretation, and hands-on exposure to real-world data analysis. The curriculum builds upon traditional multivariate statistical analysis and unsupervised learning, extending to modern machine learning topics. Topics include clustering, dimension reduction, matrix completion, factor analysis, covariance analysis, and graphical models. Additionally, advanced data structures such as text and graph data are covered. The course prioritizes the fundamental statistical principles integral to machine learning, demonstrated through the analysis and interpretation of numerous datasets.

prereq: (STAT 3701 or STAT 3301) and (STAT 4101 or STAT 5101 or MATH 5651)

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