Summer 2023
(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: (06/05—08/11 ) , Session #2: (06/05—07/07) , Session #3: (06/05—07/27) , Session #4: (07/10—08/09)
GRADUATE COURSES  SUMMER 2023
Course  Section  Course Title & Session  Course Day & Time  Rm #  Instructor 
Math 436601  16695  Numerical Linear Algebra (Session #2) 
MTWThF, 10AM—Noon (Synchronous/On Campus Exams)  TBA  J. He 
Math 436602  16918  Numerical Linear Algebra (Session #2) 
MTWThF, 10AM—Noon (Asynchronous/On Campus Exams)  TBA  J. He 
Math 4377 / Math 6308  10113  Advanced Linear Algebra I (Session #2) 
MTWThF, Noon—2PM (F2F)  S 116  M. Ru 
Math 4378 / Math 6309  10697  Advanced Linear Algebra II (Session #4) 
MTWThF, Noon—2PM (F2F)  CBB 104  A. Török 
Math 4389  15015 
Survey of Undergraduate Math 
MTWThF, 10AM—Noon (F2F)  S 114  D. Blecher 
Course  Section  Course Title  Course Day & Time  Instructor 
Math 5341  12259  Mathematical Modeling (Session #2) 
(online)  J. He 
Math 5383  12920  Number Theory (Session #2) 
(online)  M. Ru 
Math 5389  11215  Survey of Mathematics (Session #2) 
(online)  G. Etgen 
Math 5397  TBA  Survey of Mathematics (Session #4) 
(online)  TBA 
Course  Section  Course Title  Course Day & Time  Rm #  Instructor 
Math 6308 
12768  Advanced Linear Algebra I (Session #2) 
MTWThF, Noon—2PM  S 116  M. Ru 
Math 6309 
12769  Advanced Linear Algebra II (Session #4) 
MTWThF, Noon—2PM  CBB 104  A. Török 
Math 6386 01 
12500  Big Data Analytics (Session #3) 
Fr, 3—5PM  SEC 201  D. Shastri 
Course Details
SENIOR UNDERGRADUATE COURSES
Prerequisites:  MATH 2318, or equivalent, and six additional hours of 30004000 level Mathematics. 
Text(s):  Intro to Applied Linear Algebra: Vectors, Matrices, & Least Squares. ISBN: 9781316518960 
Description:  Conditioning and stability of linear systems, matrix factorizations, direct and iterative methods for solving linear systems, computing eigenvalues and eigenvectors, introduction to linear and nonlinear optimization. 
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Math 4377  Advanced Linear Algebra I


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. 
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Math 4378  Advanced Linear Algebra II


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. 
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Math 4389  Survey of Undergraduate Math


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. 
ONLINE GRADUATE COURSES
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MATH 5341  Mathematical Modeling


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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MATH 5383  Number Theory


Prerequisites:  Graduate standing. 
Text(s):  TBA 
Description:  TBA 
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MATH 5389  Survey of Mathematics


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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MATH 5397  Selected Topics in Mathematics


Prerequisites:  Graduate standing 
Text(s):  Instructor's notes 
Description:  TBD 
GRADUATE COURSES
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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) 
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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. 
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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):  TBA 
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. 