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.
3 units · Letter or Credit/No Credit
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.
Offered in Winter 2027 at Stanford University.