MATH 6397 Computational and Mathematical Methods in Data Science (Spring 2024)
News and Course Organization
Important information for the course Computational and Mathematical Methods in Data Science (MATH 6397 — 19706) will be posted on this page. Please visit it on a regular basis. Check the syllabus regularly for any important updates. It is the students responsibility to be aware of additional course policies presented by the instructor during class.Course Description
Rapid advancements in machine learning and data science have been fueled by significant advances in computing. In the age of big data, there is a push to integrate model-based and data-driven frameworks to enable real-time decision-making. This leads to the notion of digital-twin technologies. Digital twins provide computational representations of real-world assets that allow us to, for example, monitor the health and behavior of an asset. The ability to perform inference, prediction, as well as uncertainty quantification in near real-time speeds is key for assimilating data that can update and inform digital twin systems.This course provides students with the mathematical background needed to analyze, implement, and further develop numerical methods at the heart of data-enabled sciences. It is geared towards students who are interested in strengthening their theoretical foundation and honing their skills as a computational scientist and computational mathematician in the emerging field of data science and machine learning. We will review traditional approaches and explore state-of-the-art methods.
This course will be a hands-on experience; while the classes will cover both theory and implementation aspects, the main focus of the assignments will be on implementation aspects. Students will learn how to write mathematical code to solve “simple” data science problems. The focus is not to apply existing methods but rather to understand the foundational concepts by implementing mathematically sound methods from scratch. This will enable students to better understand when modern machine learning methods will work, and when they will fail. Students are free to use their preferred programming language. This course will also touch up on topics in numerical analysis, numerical linear algebra, and optimization applied to machine learning and data science.
Instructor
Andreas Mang (andreas at math dot uh dot edu).Course Material
Course material and homework assignments will be made available on Canvas. Students will be assessed through practical and theoretical homework assignments and projects.Syllabus
A tentative version of the syllabus can be downloaded here.Location and Time
AH 302, MoWe 4:00PM to 5:30PM.Mental Health and Wellness Resources
The University of Houston has a number of resources to support students' mental health and overall wellness, including CoogsCARE and the UH Go App. UH Counseling and Psychological Services (CAPS) offers 24/7 mental health support for all students, addressing various concerns like stress, college adjustment and sadness. CAPS provides individual and couples counseling, group therapy, workshops and connections to other support services on and off-campus. For assistance visit https://uh.edu/caps, call 713-743-5454, or visit a Let’s Talk location in-person or virtually. Let’s Talk are daily, informal confidential consultations with CAPS therapists where no appointment or paperwork is needed.The Student Health Center offers a Psychiatry Clinic for enrolled UH students. Call 713-743-5149 during clinic hours, Monday through Friday 8 a.m. - 4:30 p.m. to schedule an appointment.
The A.D. Bruce Religion Center offers spiritual support and a variety of programs centered on well-being.
Need Support Now? If you or someone you know is struggling or in crisis, help is available. Call CAPS crisis support 24/7 at 713-743-5454, or the National Suicide and Crisis Lifeline: call or text 988, or chat 988lifeline.org.