Course Schedule and Catalog

Department: Electrical & Computer Eng.

Meeting: 9:00AM - 9:50AM MWF (22-AUG-2022 - 2-DEC-2022) 

Part of Term: Full Term

Grade Mode: Standard Letter

Course Type: Lecture

Language of Instruction: Taught in English

Method of Instruction: Face to Face

Credit Hours: 3

 

Section Max Enrollment: 30

Section Enrolled: 0

Total Cross-list Max Enrollment: 30

Total Cross-list Enrolled: 7

Enrollment data as of: 26-APR-2024 8:39AM

 

 

Final Exam: No Final Exam

 

Description: Algorithmic procedures for working with data have been developed by re-searchers from a wide range of areas. These include theoretical computer science (TCS), numerical linear algebra (NLA), statistics, applied mathematics, data analysis, machine learning, etc. As a consequence of the multi-disciplinarity of the area, researchers often fail to appreciate the underlying connections and the significance of contributions developed outside their own area. In this course, rather than focusing on technical details, we will focus on highlighting for a broad, basic linear-algebra-savvy audience, the simplicity and generality of some core linear algebraic ideas. In particular, we will focus on two fundamental and much used matrix problems which have been at the center of recent developments: (1) Least Squares approximation and (2) Low-Rank Matrix Approximation. A key tool for achieving this goal are randomized algorithms which originated in TCS. Cross-list: ELEC 506.