C395/C895 Algorithmic and Human Decision-Making - MIT Schwarzman College of Computing

Offered under: 6.C395, 14.C395
Term(s): Spring
Level: Undergraduate, Graduate
Units: 12
Prerequisite: 6.3700 or (6.3900 or 6.C01) and (14.30 or 18.600)
Instructors: Sendhil Mullainathan (EECS/Economics)

Explores how to design machine learning and AI algorithms that enhance human decision-making and tackle real-world societal challenges. Combines statistical ML frameworks with behavioral economics, focusing on applications in areas such as education, finance, economic mobility, and policy. Emphasizes identifying high-impact problems and building effective, reliable solutions. Students taking the graduate version will complete additional assignments.

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