Math 4389

Survey of Undergraduate Math
(Session #4)

Course/Section

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Course Title

Course Day & Time 

Instructor

Course/Section 

Class #

Course Title

Course Day & Time

Rm #

Instructor

Text(s): While lecture notes will serve as the main source of material for the course, the following book constitutes a great reference:

• ”An Introduction to Statistical Learning (with applications in R)” by James, Witten et al. ISBN: 978-1461471370
• ”Neural Networks with R” by G. Ciaburro. ISBN: 978-1788397872

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)

• 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. 

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)

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 à 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 à 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 https://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.