C03 Machine Learning Challenge for Biomedical Discoveries

Offered under: 6.S099
Term(s): IAP only
Level: Undergraduate
Units: 6 (P/D/F)
Prerequisite: Programming (e.g., Python) at the level of 6.1010. Recommended: 6.3720, 6.3900, or 6.3730[J]/IDS.012[J]. No background in biology required.
Instructors: Caroline Uhler (EECS), Paul Blainey (Biological Engineering), Jonathan Weissman (Biology)

Scientists are increasingly turning to machine learning challenges, or competitions that require participants to build and evaluate machine learning models over a given period of time to solve a problem. The Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard organizes global machine learning challenges to leverage machine learning for solving key biomedical problems and to help prioritize what experiments biologists could run next – creating the next steps in disease diagnostics and treatment.

In this class, students will participate in the Schmidt Center’s machine learning challenge and apply their machine learning skills to help solve a key biomedical problem.

Students will learn the basics of genomics and data analysis needed to succeed in the challenge. Top-scoring submissions will be validated in a lab at the Broad Institute, and winners will be eligible for monetary prizes and paper authorship.