ELE538B: Sparsity, Structure and Inference

Yuxin Chen, Princeton University, Spring 2017

Course Description

This is a graduate level course covering various aspects of sparsity—or more broadly, low-dimensional structures—that arise in large-scale data science and machine learning applications. We will introduce a mathematical theory for sparse representation, and will cover several fundamental inference/estimation/learning problems that are built upon sparse modeling, including sparse linear regression, graphical models, compressed sensing, matrix completion, robust principal component analysis, super resolution, etc. We will focus on designing algorithms — in particular, optimization-based methods — that are effective in both theory and practice.

Lectures

Mon, Wed 9:30 AM - 10:50 AM.
Room: Friend Center 003

Teaching Staffs

Instructor: Yuxin Chen, C330 Equad, yuxin dot chen at princeton dot edu
Teaching assistant: Yeohee Im, F210-E3 Equad, yeoheei at princeton dot edu

Announcements

4/9: Homework 3 is out. It is due on Wednesday, Apr 26.
3/6: Project proposal due on Friday, Mar 17.
3/6: Homework 2 is out. It is due on Wednesday, Mar 29.
3/5: Yeohee's office hour will be changed to 4:30 - 5:30pm Tuesday starting from this week.
2/20: 3rd review session to be held at 11am-11:30am on Friday, Feb 24 in B205 Equad. The topic is CVX.
2/14: Homework 1 is out. It is due on Wednesday, Mar 1.
2/13: 2nd review session to be held at 11am-12pm on Friday, Feb 17 in B205 Equad. The topic is basic probability.
2/7: 1st review session to be held at 11am-12pm on Friday, Feb 10 in B205 Equad. The topic is basic linear algebra.