This course covers statistical techniques underlying modern machine learning. Topics include prediction methods (linear models, trees and forests, deep neural networks), model evaluation (cross-validation, calibration, conformal prediction), optimization (convexity, stochastic methods, adaptive metrics), and generative models (language models, diffusion models, variational autoencoders).
3 units · Letter or Credit/No Credit
This course covers statistical techniques underlying modern machine learning. Topics include prediction methods (linear models, trees and forests, deep neural networks), model evaluation (cross-validation, calibration, conformal prediction), optimization (convexity, stochastic methods, adaptive metrics), and generative models (language models, diffusion models, variational autoencoders).
Offered in Winter 2027 at Stanford University.