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 4366-01 | 16695 | Numerical Linear Algebra (Session #2) |
MTWThF, 10AM—Noon (Synchronous/On Campus Exams) | TBA | J. He |
Math 4366-02 | 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 |
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SENIOR UNDERGRADUATE COURSES
Prerequisites: | MATH 2318, or equivalent, and six additional hours of 3000-4000 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
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Prerequisites: | 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 |
Description: | Syllabus: Chapter 1, Chapter 2, Chapter 3, Chapter 4 (4.1-4.4), Chapter 5 (5.1-5.2) (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
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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 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 4389 - Survey of Undergraduate Math
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Prerequisites: | MATH 3330, MATH 3331, MATH 3333, and three hours of 4000-level 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
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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
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Prerequisites: | Graduate standing. |
Text(s): | TBA |
Description: | TBA |
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MATH 5389 - Survey of Mathematics
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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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MATH 5397 - Selected Topics in Mathematics
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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 3000-4000 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 (4.1-4.4), Chapter 5 (5.1-5.2) (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 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
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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 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. |