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Spring 2027
Data Science in Medical Physics
Covers statistics and machine learning concepts for medical physics research, including probability, inference, regression, and neural networks applied to quality assurance.
offering recorded3 credits
Summary
What you’ll be able to do
Analyze experimental data by calculating mathematical expectation, co-variance, and variance for both discrete and continuous random variables from experimental measurements and determine statistical parameters using parametric statistical models
Draw proper statistical conclusions from experimental data by constructing confidence intervals for estimated statistical parameters from data at a given statistical significance level and formulate a statistical hypothesis for drawing possible conclusions. Construct test statistics to perform hypothesis testing using the P-value method and the rejection-zone method at a given significance level
Perform supervised learning using linear regression methods and interpret the regression results for experimental data as well as perform supervised learning using logistic regression methods for classification tasks such as image segmentation
Perform unsupervised learning using K-means clustering, Expectation-Maximization (EM) methods, and kernel tricks to recognize underlying patterns in experimental data as well as perform supervised learning using deep neural network modeling and backpropagation learning schemes
Source: UW Guide
Grade history
↗ average GPA
00 letter grades
No recorded grade history.
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Prerequisites
Course map(PHYSICS/B M E/H ONCOL/MED PHYS 501 and B M E/MED PHYS 573) or (STAT/MATH 309 or 431) or graduate/professional standing
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- take one
- graduate/professional standing
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Prerequisite text tree
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Professors
Spring 2027No course-specific feedback yet.
Historical instructors & teaching patterns
MARTIN WAGNER is recorded teaching in Spring 2026. Recorded history may be incomplete and does not establish a future schedule.
TYLER BRADSHAW is recorded teaching in Spring 2026. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 0 / 30 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Concepts and principles of statistics and machine learning for medical physics-related research problems. Topics covered include probability and independence, discrete and continuous random variables and statistical distributions, random sampling and central limit theorem, inference for means, variances, proportions, moment generating functions, maximum likelihood, hypothesis testing, ANOVA, linear regression, correlation and basic design of experiments with application to quality assurance, reliability, and reproducibility.
difficulty & workload
No workload feedback recorded.
Topics
- Probability, random variables, distributions, and central limit theorem
- Statistical inference, maximum likelihood, hypothesis testing, and ANOVA
- Linear regression, correlation, design of experiments, and applications to QA and reliability
- Unsupervised learning with K-means, EM, and kernel tricks; supervised deep neural networks
Skills
- Calculate statistical parameters for discrete and continuous random variables using parametric models
- Construct confidence intervals and perform hypothesis testing using P-value and rejection-zone methods
- Apply linear and logistic regression for supervised learning and classification tasks
- Use K-means, EM, kernel tricks, and deep neural networks with backpropagation for pattern recognition
Grades
Instructor
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Sources & history
UW–Madison
Catalog & offerings
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Selected offering source records
Data Science in Medical Physics
Recorded 2026-10-14Raw records
[
{
"run_id": "20261014T203208-0851c9a1",
"semester": "1274",
"observed_at": "2026-10-14 17:50:09.728220+00:00",
"offering_id": "1274:621:022359",
"course_id": "MEDPHYS 674",
"course_uid": "course_73b6fa15a68936359e8c9653",
"term_id": "1274",
"source_course_id": "022359",
"source_subject_id": "621",
"title": "Data Science in Medical Physics",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Spring"
}
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Madgrades
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Explore recorded gradesDataset & model
View the dataset on Hugging Face Recorded 2026-10-14
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "11499f31af5e09fa1fb3d3424ed123c15e55ac58d34e86bedc4e489b19a763c6",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}