CSE 494, CSE/CBS 598: Numerical Linear Algebra for Data
Exploration

Instructor: 
Dr. 
Email: 

Office hours: 
MW 2:30pm—4:00pm 
Telephone: 
4807277451 
Overview
Linear Algebra
has contributed many methods for handling very large quantities of numerical
data. This course will cover linear algebra techniques useful in data
exploration. Topics include least squares, matrix decompositions, tensor
decompositions, eigenvalue and generalized eigenvalue decompositions, and their applications to data
mining, machine learning, computer vision, information retrieval,
bioinformatics, and computer graphics. Both theoretical and algorithmic aspects
are considered.
Prerequisite: Basic linear algebra skills.
Textbook: Matrix Methods in Data Mining and Pattern
Recognition, by Lars Elden,
Topics to be covered:
·
Linear algebra background
o
Vectors and Matrices
o
Linear Systems and Least Squares
o
Singular Value Decomposition
o
Reduced Rank Least Squares Models
o
Tensor Decomposition
o
Clustering and NonNegative Matrix Factorization
·
Applications
o
Classification of Handwritten Digits and face
images
o
Text Mining
o
Page Ranking for a Web Search Engine
o
Automatic Key Word and Key Sentence Extraction
o
Massive data compression in computer graphics using
tensor SVD
o
Clustering and classification of Microarray gene expression data
o
Gene expression pattern image classification and
retrieval