Selected Publications


The goal of this undergraduate course is to introduce data analysis from the machine learning perspective. Students will gain familiarity with the workings of common machine learning models and will learn how noise and bias in the data affect their results. The goal of the course is to equip students with required skills to understand and design new learning algorithms!

  • CSE 597: Large-Scale Machine Learning: Mathematical Foundations and Applications [Syllabus]

This graduate-level course will aim to cover various mathematical aspects of big and high-dimensional learning arising in data science and machine learning applications. The focus will be on building a principled understanding of randomized methods via a mixture of empirical evaluations and mathematical modeling. Specifically, we will explore large-scale optimization algorithms for both convex and non-convex optimization, dimension reduction and random projection methods, large-scale numerical linear algebra, sparse recovery and compressed sensing, low-rank matrix recovery, convex geometry and linear inverse problems, empirical processes and generalization bounds, as well as theory and optimization landscape of neural networks, etc.