36-669 Statistical Genomics and High Dimensional Inference

9 units, Carnegie Mellon University

The field of computational and statistical genomics focuses on developing and applying computationally efficient and statistically robust methods to sort through increasingly rich and massive genome wide data sets to identify complex genetic patterns, gene interactions, and disease associations. Because the genome is vast, analytical approaches require high dimensional statistical approaches such as multiple testing, dimension reduction techniques, regularization and high dimensional regression analysis, best linear unbiased prediction models, networks and deep learning. In this course, we will motivate these topics using data obtained from the human genetic and genomic literature. No prior knowledge in biology is required.

Prerequisites: none listed

Offered: Spring 2026

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