COMP642
Numerical Estimation

Course Description

Course Objectives:
Estimation theory is a product of need and technology. As a result, it is an integral
part of many branches of science and engineering, such as statistics, signal processing,
communications, control and navigation. The techniques are used in several areas in
computer science, such as machine learning, graphics, image processing, robotics, bioinformatics,
and computer vision etc.
This course will focus on the design and implementation of efficient and reliable
computer algorithms in this area.
Topics:
 Elements of numerical linear algebra;
 Basic results of probability theory and statistics;
 SVD, randomized SVD, and PCA
 Ordinary least squares estimation
 Large sparse least squares problems;
 Generalized least squares estimation;
 Total least squares estimation;
 L2norm regularized least squares estimation;
 Nonlinear least squares estimation;
 Maximumlikelihood estimation;
 Minimum mean square error estimation;
 Maximum a posteriori estimation;
 Kalman filtering;
 L1norm regularized least squares estimation (LASSO);
 Integer least squares estimation
Prerequisites: COMP 350A Numerical Computing or
equivalent, MATH 323 Probability Theory or equivalent, a good introductory
matrix theory course.
COMP 540 Matrix Computations is helpful, but not required.

