Data Science in Medical Physics
Covers statistics and machine learning principles for medical physics research, including inference, regression, and experimental design.
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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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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.
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{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "80af5a420f74004d4bbe343fe569d037fcdb988c5a4d3dfedfc61cd4a9fa977a",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}