EdusalsaDiscover Your Stanford

CS 329D

Machine Learning Under Distributional Shifts

  • Not Offered

3 units

Letter or Credit/No Credit

The progress of machine learning systems has seemed remarkable and inexorable ¿ a wide array of benchmark tasks including image classification, speech recognition, and question answering have seen consistent and substantial accuracy gains year on year. However, these same models are known to fail consistently on atypical examples and domains not contained within the training data. The goal of the course is to introduce the variety of areas in which distributional shifts appear, as well as provide theoretical characterization and learning bounds for distribution shifts. Prerequisites: CS229 or equivalent. Recommended: CS229T (or basic knowledge of learning theory).

Course Prequisites

Sign Up

To save CS 329D to your course bucketlist

Already Have An Account? Log In