A large number of problems in machine learning and signal processing require to
extract information from data that take the form of matrices or tensors.
We will discuss general mathematical and algorithmic tools to address these problems:
spectral algorithms, semidefinite programming relaxations, nonconvex optimization
methods.
Specific topics include: clustering; graph data analysis; matrix completion; graph localization;
group synchronization; dimensionality reduction and manifold learning.
Class Times and Locations
Hewlett 201
MonWed, 1:302:50pm
