Course Schedule - Fall Semester 2022

     

Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).
Additional information available here.

ELEC 406 001 (CRN: 14723)

LINEAR ALGEBRA FOR DS

Long Title: LINEAR ALGEBRA FOR DATA SCIENCE
Department: Electrical & Computer Eng.
Instructor: Antoulas, Athanasios C
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
Course Syllabus:
Course Materials: Rice Campus Store
 
Section Max Enrollment: 30
Section Enrolled: 0
Total Cross-list Max Enrollment: 30
Total Cross-list Enrolled: 7
Enrollment data as of: 19-JUL-2024 9:18AM
 
Additional Fees: None
 
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.