Fall 1999
CS 383C / CAM 383C / Math 383E
"Numerical Linear Algebra"
T-Th 9:30-11am
TAY 3.144
Prof. Inderjit Dhillon
Matrix Computations arise in a varied number of applications, such as,
quantum chemistry computations, statistics, economics, data mining,
information retrieval etc. This first year graduate course focuses
on some of the fundamental computations that occur in these applications.
Topics studied will include computer solutions of linear systems (both
direct and iterative methods), least squares problems, dense &
sparse eigenvalue problems as well as SVD computations.
A substantial portion of this class will be research projects, where
students will have freedom of choosing a well-defined problem of
their choice, or will be assigned a suitable interesting project.
Pre-requisites for this course are a good knowledge of undergraduate
linear algebra, and some mathematical sophistication (students should
know how to write a mathematical proof). No previous numerical
programming is required, although some such experience would be helpful.
The instructor will teach a new graduate course on "Large-Scale
Data Mining" in Spring 2000, and the present course should serve
as valuable background material.
More course information is available at
http://www.cs.utexas.edu/users/inderjit/courses/cs383c.html