Reliability and Validity in Artificial Intelligence
Course Description
This course examines the principles and methods required to make artificial intelligence (AI) systems reliable and scientifically sound. Topics include evaluation and benchmarking, notions of validity, distribution shift, causality, predictive inference, AI-assisted statistical inference, data attribution, and beyond. Problem sets will involve both mathematical components and coding projects to see the practical effects of the methods we develop.
STATS 357
Units: 3
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Typically Offered
Not Available
Grading Basis
Letter or Credit/No Credit
Class Level
Graduate
Instructional Mode
In Person
Gen Ed Requirement(s)
None