MTH 995-001: Introduction to Compressive Sensing and the Analysis of Big Data
|Time and Place:||T Th 3:00 pm -- 4:20 pm, in A332 WH|
|Office Hours:||F 9:00 am -- 10:00 am, and by appointment|
This class will focus on the rigorous analysis of practical algorithms for both compressive sensing, and the analysis of large and high dimensional data sets. Topics discussed will include (time permitting): Semidefinite programming, locally sensitive hashing, manifold models for data, fast sparse Fourier transforms, the approximation of functions of many variables, metric space embeddings, and various applications of random matrices (including Johnson-Lindenstrauss embeddings, nearly-isometric embeddings of smooth submanifolds of RN, and the restricted isometry property).
Course website for MTH995-001:
The course website has the course schedule, the syllabus, and supplementary reading. Papers covered in class will be posted there.
A Mathematical Introduction to Compressive Sensing, by Simon Foucart and Holger Rauhut. Springer, ISBN 978-0-8176-4947-0
This book is an excellent reference. I recommend it highly. In addition, we will be covering several papers during the semester. These papers will be posted on the course schedule before they are discussed.