3.0

credits

Average Course Rating

This course teaches machine learning methods that 1) consider data distribution shift and 2) represent and quantify the model uncertainty in a principled way. The topics we will cover include machine learning techniques that deal with data distribution shift, including domain adaptation, domain generalization, and distributionally robust learning techniques, and various uncertainty quantification methods, including Bayesian methods, conformal prediction methods, and model calibration methods. We will introduce these topics in the context of building trustworthy machine learning solutions to safety-critical applications and socially-responsible applications. For example, a typical application is responsible decision-making under uncertainty in non-stationary environments. So we will also introduce concepts like fair machine learning and learning under safety constraints, and discuss how robust and uncertainty-aware learning techniques contribute to such more desired systems. Students will learn the state-of-the-art methods in lectures, test their understanding in homeworks, and apply these methods in a project. Required course background: 601.475/675 Machine Learning. Students may receive credit for only one of 601.487/687, and may not take this course after taking EN.601.787.