CS 6962 Decomposition Techniques for Computational Data-Enabled Science and Engineering
Researchers in a variety of fields collect measurements, observe data,
perform simulations and use a wide range of techniques to describe,
classify, analyze and draw conclusions from these data. Selecting
appropriate techniques and understanding their advantages and
disadvantages is an important component of data analysis. In
particular, in this age of big data, large data sets provide distinct
challenges given that many of the general techniques for small data
sets do not scale to larger problems and are often prohibitively
expensive from a computational perspective.
In this class, we will survey several data decomposition techniques
for computational data-enabled science and engineering applications.
Lectures: Tues/Thurs, 12:25pm - 1:45pm, WEB L105 - ROOM CHANGED to 2760 WEB
Professor Johnson: Tuesdays between 3:00
- 4:00 p.m. and by appointment
Dr. Tushar Athawale (tushar.athawale [at] sci.utah.edu) , Mondays/Wednesdays between 3:00 - 4:30 p.m. in WEB 2807
cs6962 [at] list.eng.utah.edu: class list. Emails to this list go to all the students. Only registered users can email to the list.
teach-cs6962 [at] list.eng.utah.edu: instructor list. Emails sent to this list go to all the instructors.
When class material questions are sent to the instructor email list, we will isolate the question and post the response to the class list (so that all can learn from both the question and answer).
The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities. If you will need accommodations in the class, reasonable prior notice needs to be given to the Center for Disability Services, 162 Olpin Union Building, 581-5020 (V/TDD). CDS will work with you and the instructor to make arrangements for accommodations.
All written information in this course can be made available in alternative format with prior notification to the Center for Disability Services.