CSE 519T Advanced Machine Learning

Fall 2019: Robust learning and statistics
Instructor: Brendan Juba
Tuesday/Thursday 1pm-2:20pm, Cupples I 115

Course Piazza board. Please ask all questions that are not of a personal nature in a public post to the Piazza board.

Description: An introduction to the problems of learning and inference when some portion of the data consists of arbitrary "outliers," including an introduction to task formulations that allow the "inliers" (data of interest) to comprise a minority fraction. We will examine both tractable algorithms for these problems and their statistical requirements. Mathematical maturity and general familiarity with machine learning is required.

Prerequisite: CSE 517A, mathematical maturity

Grades and assignments: Students will each present one of the papers from the list below, and grades will be based on these presentations. It is expected that students will attend others' presentations and participate in discussion of the presentations/Q&A.


To be filled in as the semester progresses:

List of Papers

unsupervised (subspace recovery) mean/covariance estimation, etc. mixture of Gaussians sparse models fast estimators convex/stochastic optimization regression Bayesian networks hardness minority inliers