Semiparametric Methods in Data Science
Advanced course covering statistical convergence, M-estimation, Hilbert space, and deriving nuisance tangent spaces and efficient influence functions in semiparametric models.
Summary
Recent recorded grades — Fall 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades).
What you’ll be able to do
Derive the nuisance tangent space, its complement, and the corresponding efficient influence function in semiparametric models
Apply a variety of semiparametric methods and models in applications ranging from biomedical studies to social sciences
Perform machine learning algorithms for estimating nuisance functions in software such as R and/or Python
Source: UW Guide 1· UW Guide 2
Grade history
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Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
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- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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Student experience
the class
Recent recorded grades — Fall 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Statistical convergence modes, M-estimation, and Hilbert space basics
- Nuisance tangent space derivation and efficient influence functions in semiparametric models
- Machine learning estimation of nuisance functions in R and Python
- Semiparametric models for missing data, causal inference, dimension reduction, and precision medicine
Skills
- Deriving nuisance tangent spaces, complements, and efficient influence functions in semiparametric models
- Estimating nuisance functions using machine learning methods implemented in R or Python
- Applying semiparametric methods and models in biomedical and social science contexts
Grades
Latest available · Fall 2024— not enough history to project Spring 2027.
Grade distribution · % of letter grades
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Sources & history
Catalog & offerings
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
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"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "543cd130ad3cc2d03087f32553a983026b620f36d74edd4c80e623f701556ae1",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}