36-319 Statistics and Machine Learning for the Physical Sciences

9 units, Carnegie Mellon University

In this course, we will learn about new methodological research at the intersection of statistics, machine learning, and AI with applications in the physical sciences (including meteorology, remote sensing, astronomy, and particle physics). Students will see examples of how core statistical ideas (confidence sets, hypothesis testing, two-sample testing, and uncertainty quantification) play a key role in cutting-edge research in predictive inference, probabilistic forecasting, anomaly or signal detection, and likelihood-free (aka simulator-based) inference. Homework assignments will include assigned reading, theory problems, and some simple computer exercises. Students will work with real-world data during TA-led computer labs using Python/PyTorch and Jupyter notebooks to apply the skills and knowledge acquired throughout the program. (Prior exposure to Python programming is not required.)

Prerequisites: 36-220 or 36-225 or 15-259 or 36-219 or 36-218 or 36-235

Offered: Fall 2026

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