Contacts

Graduate Menu

Graduate Resources

Course Descriptions

16:642:550 - Linear Algebra and Applications

Fall 2026

Zheng-Chao Han

Course Description:

This is a course for graduate students in science, engineering, and statistics. It will cover matrix decomposition methods, solution of linear systems, spectral decomposition, singular value decomposition. Applications of these algorithms will be an integral part of the course.

Text:

Gilbert Strang: Linear Algebra and Learning from Data

Prerequisites:

Familiarity with undergraduate level linear algebra/matrix operations

****************************************************************************************************

Fall 2025

Liping Liu

Course Description:

This is a course aiming at graduate students in science, engineering, and statistics. The course covers Gauss elimination, vector spaces, linear transformations, determinants, eigenvalues and eigenvectors, with applications to least squares approximations, discrete Fourier transform, differential equations, Markov chain, and principal component analysis.

Text:

Gilbert Strang: Linear Algebra and Applications

Prerequisites:

The equivalent of 250 or 350

****************************************************************************************************

Spring 2025

Michael Vogelius

Course Description:

A general linear algebra course with a focus on principal component analysis (SVD) and its applications.

Text:

Gilbert Strang: Linear Algebra and Learning from Data

Prerequisites:

An undergraduate course similar to our 250

*************************************************

Fall 2023

Zheng-Chao Han

Course Description:

This is a course for graduate students in science, engineering, and statistics. It will cover matrix decomposition methods, solution of linear systems, spectral decomposition, singular value decomposition and applications in compressed sensing, probability and statistics, and optimization.

Text:

Charu C. Aggarwal, published by Springer, 2020.  Springer allows faculty and students to get electronic versions free of charge.

Prerequisites:

640:250 or 640:350 or equivalent

*************************************************

Fall 2022

Michael Vogelius

Course Description:

Covers matrix decomposition methods, solution of linear systems, spectral decomposition, singular value decomposition and applications (e.g., time permitting, compressed sensing and a simple page rank algorithm)

Text:

Gilbert Strang: Linear Algebra and Learning from Data

Prerequisites:

the equivalent of 250 or 350

*************************************************

Fall 2021

Tsai, Li-Cheng

Course Description:

This is a course aiming at graduate students in science, engineering, and statistics. The course covers Gauss elimination, vector spaces, linear transformations, determinants, eigenvalues and eigenvectors, with applications to least squares approximations, discrete Fourier transform, differential equations, Markov chain, and principal component analysis. The course will be accompanied by labs.

Text:

Linear Algebra with Applications, by W. Keith Nicholson (Open access under Creative Commons License)

Prerequisites:

Familiarity with matrices, vectors, complex numbers, and mathematical reasoning at the level of advanced undergraduate mathematics courses.

 

Schedule of Sections

 

Previous semesters: