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Department of Mathematics

Graduate Studies

2026 - Summer Semester

Disclaimer: Be advised that some information on this page may not be current due to course scheduling changes. Please view either the UH Class Schedule page or your Class Schedule in for the most current/updated information. Click this link to access the .

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GRADUATE ONLINE COURSES

Course/Section

Class #

Course Title

Course Day & Time 

Instructor
Math 5341-01  11744 Mathematical Modeling
 (Session #2)		 Asynchronous - Online J. He
Math 5383-02  14074 Number Theory
 (Session #2)		 Asynchronous - Online M. Ru
Math 5389-02 14277 Survey of Mathematics
 (Session #2)		 Asynchronous - Online G. Etgen

 

GRADUATE COURSES

Course/Section 

Class #

Course Title

Course Day & Time

Rm #

Instructor
Math 6308-01 12091 Advanced Linear Algebra I (Session #2)  MTWThF, 10AM鈥擭oon CBB 104  M. Perepelitsa
Math 6309-01
 12092 Advanced Linear Algebra II (Session #4)  MTWThF, 10AM鈥擭oon SEC 201 A. Torok


MSDS GRADUATE COURSES

(MSDS Students Only - Contact Ms. Tierra Kirts for specific class numbers) 

Course/Section 

Class #

Course Title

Course Day & Time

Rm #

Instructor
Math 6386-01
  not shown to students  Big Data Analytics
(Session #3)		 Friday, 3鈥�5PM online D. Shastri
 
 

 

ONLINE GRADUATE COURSES

 

MATH 5341 - Mathematical Modeling  

Prerequisites: Graduate standing. Calculus III and Linear Algebra
Text(s):  , Boyd and Vandenberghe, Cambridge University Press, 2018  (free download) 
Course description:
  • Platforms: MS Teams and Blackboard.
  • Technology Requirements: computer, internet, microphone and webcam.
  • Overview: The course introduces vectors, matrices, and least squares methods, related topics on applied linear algebra that are behind modern data science and other applications, including document classification, prediction model from data, enhanced images, control, state estimation, and portfolio optimization. We will review vectors and matrices in the first two weeks, and then focus on least squares and more advanced examples and applications in the following two and half weeks. 
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MATH 5383 - Number Theory

 Prerequisites:  Graduate standing.
 Text(s):  Instructor's notes
Course description: Divisibility and factorization, linear Diophantine equations, congruences and applications, solving linear congruences, primes of special forms, the Chinese remainder theorem, multiplicative orders, the Euler function, primitive roots, quadratic congruences, representation problems and continued fractions.
 

MATH 5389 - Survey of Mathematics

Prerequisites: Graduate standing
Text(s): Instructor's notes
Course description: A review and consolidation of undergraduate courses in linear algebra, differential equations, analysis, probability, and astract algebra. Students may not receive credit for both MATH 4389 and MATH 5389.
 

MATH 5397 - Selected Topics in Mathematics

Prerequisites: Graduate standing
Text(s): Instructor's notes
Course description: TBD
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GRADUATE COURSES

 

MATH 6308 - Advanced Linear Algebra I

Prerequisites: Graduate standing. MATH 2331 and MATH 3325, and three additional hours of 3000-4000 level Mathematics.
Text(s): Linear Algebra, 5th Edition by  Stephen H. Friedberg, Arnold J. Insel, Lawrence E. Spence. ISBN: 9780134860244

Course description: The general theory of Vector Spaces and Linear Transformations will be developed in an axiomatic fashion. Determinants will be covered to study eigenvalues, eigenvectors and diagonalization.

Grading:  There will be  three  Tests and the Final. I will take the two highest test scores (60%) and the mandatory final (40%). Tests and the Final are based on homework problems and material covered in class.

Syllabus:  Chapter 1, Chapter 2, Chapter 3, Chapter 4 (4.1-4.4), Chapter 5 (5.1-5.2) (probably not covered)
 

MATH 6309 - Advanced Linear Algebra II

Prerequisites: Graduate standing. Math 4377 or Math 6308
Text(s): Linear Algebra, 5th edition, by Friedberg, Insel, and Spence, ISBN: 9780134860244
Course description: The instructor will cover Sections 5-7 of the textbook. Topics include: Eigenvalues/Eigenvectors, Cayley-Hamilton Theorem, Inner Products and Norms, Adjoints of Linear Operators, Normal and Self-Adjoint Operators, Orthogonal and Unitary Operators, Jordan Canonical Form, Minimal Polynomials.
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MATH 6386 - Big Data Analytics

Prerequisites: Graduate standing. Students must be in the Statistics and Data Science, MS program. Linear algebra, probability, statistics, or consent of instructor. 

Text(s):

  • "Learning Spark: Lightning-Fast Data Analytics", by Jules S. Damji, Brooke Wenig, Tathagata Das, and Denny Lee, 2020, 2nd Edition, O'Reilly Media. (LS) ISBN-13: 978149205004 [Available for free on UH Library 脿 Safari Books Online]
  • "Big Data Analytics with R: Leverage R Programming to uncover hidden patterns in your Big Data", by Simon Walkowiak, 2016, 1st Edition, Packt Publishing. ISBN-13: 9781786466457 [Available for free on UH Library 脿 Safari Books Online]
  • Mining of Massive Datasets 3rd Edition by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman 2020, 3rd Edition, Cambridge University Press. ISBN-13: 978-1108476348 [Available for free at http://www.mmds.org/]

Course description: Concepts and techniques in managing and analyzing large data sets for data discovery and modeling: big data storage systems, parallel processing platforms, and scalable machine learning algorithms.

Class notes: Computer and internet access required for course. For the current list of minimum technology requirements and resources, copy/paste/navigate to the URL /online/tech/requirements.

For additional information, contact the office of Online & Special Programs at UHOnline@uh.edu or 713-743-3327. Course instruction for this section takes place both in a classroom face-to-face environment during the scheduled time and additionally by electronic means. 
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(Updated 10/28/26)