SEMIPARAMETRIC METHODS IN DATA SCIENCE

BMI/STAT 828

Course Description

Review of statistical convergence modes, M-estimation, and basics of Hilbert space. Introduction of how to derive the nuisance tangent space, its complement, and the corresponding efficient influence function, from the geometric perspective of semiparametric models. Introduction of how to estimate nuisance functions using machine learning methods, and their implementations in R and/or Python. Introduction of a variety of semiparametric models in missing data analysis, causal inference, dimension reduction, precision medicine, semi-supervised learning, transfer learning and domain adaptation.

Prerequisties

Graduate/professional standing

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average

No change from Historical

Completion Rate

100%

No change from Historical

A Rate

100%

No change from Historical

Class Size

No change from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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