AI-Augmented Neuromedical Data Science

3 units

This course develops rigorous and practical skills for analyzing data generated by the normal and malfunctioning nervous system. Lessons emphasize producing generalizable scientific claims and clinically relevant prediction. Students learn end-to-end workflows for data curation, exploratory analysis, signal processing, statistical inference and predictive modeling, alongside clear communication of uncertainty and limitations. A central theme is the responsible use of AI assistants: students practice specifying analytical goals and constraints, curating and verifying AI-generated code and interpretations, and performing thorough critiques to identify hidden assumptions, leakage, confounding, bias and reproducibility failures. The course emphasizes best practices in computational research, ethics and scientific reasoning appropriate for high-value biomedical contexts.