Summer 2024
(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 myUH for the most current/updated information.)
Session #Regular: (TBA ) , Session #2: (06/03—07/03) , Session #3: (06/03—07/26) , Session #4: (07/08—08/07)
Graduate Courses  SUMMER 2024
(UPDATED 05/16/24)
Course/Section  Class #  Course Title & Session  Course Day & Time  Rm #  Instructor 
Math 4377 / Math 6308  10094  Advanced Linear Algebra I (Session #2) 
MTWThF, Noon—2PM (F2F, Session 2)  GAR G201  D. Labate 
Math 4378 / Math 6309  10478  Advanced Linear Algebra II (Session #4) 
MTWThF, Noon—2PM (F2F, Session 4)  S 105  M. Kalantar 
Math 4389  15269 
Survey of Undergraduate Math 
Online (Asynchronous/On Campus Exams)  online  G. Etgen 
Course/Section  Class #  Course Title  Course Day & Time  Instructor 
Math 534101  11882  Mathematical Modeling (Session #2) 
(online) Asynchronous  On Campus Exams  J. He 
Math 538301  12441  Number Theory (Session #2) 
(online) Asynchronous  On Campus Exams  M. Ru 
Math 538901  10960  Survey of Mathematics (Session #2) 
(online) Asynchronous  On Campus Exams  G. Etgen 
Course/Section  Class #  Course Title  Course Day & Time  Rm #  Instructor 
Math 6308 
12311  Advanced Linear Algebra I (Session #2) 
MTWThF, Noon—2PM  GAR G201  D. Labate 
Math 6309 
12312  Advanced Linear Algebra II (Session #4) 
MTWThF, Noon—2PM (F2F)  S 105  M. Kalantar 
(MSDS Students Only  Contact Ms. Callista Brown for specific class numbers)
Course/Section  Class #  Course Title  Course Day & Time  Rm #  Instructor 
Math 6386 
not shown to students  Big Data Analytics (Session #3) 
F, 3—5PM  TBD  D. Shastri 
 Course Details 
Senior Undergraduate Courses
Prerequisites:  MATH 2331 and MATH 3325, and three additional hours of 30004000 level Mathematics. 
Text(s):  Linear Algebra, 5th Edition by Stephen H. Friedberg, Arnold J. Insel, Lawrence E. Spence. ISBN: 9780134860244 
Description:  Syllabus: Chapter 1, Chapter 2, Chapter 3, Chapter 4 (4.14.4), Chapter 5 (5.15.2) (probably not covered) 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. 
{back to Senior Courses}
Prerequisites:  Math 4377 or Math 6308 
Text(s):  Linear Algebra, 5th edition, by Friedberg, Insel, and Spence, ISBN: 9780134860244 
Description:  The instructor will cover Sections 57 of the textbook. Topics include: Eigenvalues/Eigenvectors, CayleyHamilton Theorem, Inner Products and Norms, Adjoints of Linear Operators, Normal and SelfAdjoint Operators, Orthogonal and Unitary Operators, Jordan Canonical Form, Minimal Polynomials. 
{back to Senior Courses}
Prerequisites:  MATH 3330, MATH 3331, MATH 3333, and three hours of 4000level Mathematics. 
Text(s):  Instructors notes 
Description:  A review of some of the most important topics in the undergraduate mathematics curriculum. 
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ONLINE GRADUATE COURSES
Prerequisites:  Graduate standing. Calculus III and Linear Algebra 
Text(s): 
Textbook (free download): Introduction to Applied Linear Algebra, Boyd and Vandenberghe, Cambridge University Press, 2018 
Description: 
Course Platforms: MS Teams and Blackboard. Course Technology Requirements: Computer, internet, microphone and webcam. Course Overview:vThe 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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Prerequisites:  Graduate standing. 
Text(s):  Instructor's notes 
Description (Catalog):  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. 
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Prerequisites:  Graduate standing 
Text(s):  Instructor's notes 
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. 
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Prerequisites:  Graduate standing 
Text(s):  Instructor's notes 
Description:  TBD 
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GRADUATE COURSES
Prerequisites:  Graduate standing. MATH 2331 and MATH 3325, and three additional hours of 30004000 level Mathematics. 
Text(s):  Linear Algebra, 5th Edition by Stephen H. Friedberg, Arnold J. Insel, Lawrence E. Spence. ISBN: 9780134860244 
Description: 
Syllabus: Chapter 1, Chapter 2, Chapter 3, Chapter 4 (4.14.4), Chapter 5 (5.15.2) (probably not covered) 
{back to Graduate Courses}
Prerequisites:  Graduate standing. Math 4377 or Math 6308 
Text(s):  Linear Algebra, 5th edition, by Friedberg, Insel, and Spence, ISBN: 9780134860244 
Description: 
The instructor will cover Sections 57 of the textbook. Topics include: Eigenvalues/Eigenvectors, CayleyHamilton Theorem, Inner Products and Norms, Adjoints of Linear Operators, Normal and SelfAdjoint Operators, Orthogonal and Unitary Operators, Jordan Canonical Form, Minimal Polynomials. 
{back to Graduate Courses}
Prerequisites:  Graduate standing. Students must be in the Statistics and Data Science, MS program. Linear algebra, probability, statistics, or consent of instructor. 
Text(s): 

Description: 
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 http://www.uh.edu/online/tech/requirements. For additional information, contact the office of Online & Special Programs at UHOnline@uh.edu or 7137433327. Course instruction for this section takes place both in a classroom facetoface environment during the scheduled time and additionally by electronic means. 