- Contents and Outcomes:
This course explores modern reasoning techniques for the extraction of
information from noisy data sources, for the
integration of multiple information streams, and for
decision-making in the presence of uncertainty. While
this course will investigate these techniques often in
the context of physical sensor applications and
robotics, they are also applicable in a wide range of
other fields including mobile networking, data mining,
and control of physical processes. Students completing this course will gain an understanding of advanced methods to work with uncertain data and be able to apply them to real world problems.
Many of the techniques covered in this course are
based on probabilities and knowledge of basic
statistics is useful. Prerequisites for this
course are either Data Modeling (CSE 5301), Artificial Intelligence (CSE
5361), Robotics (CSE 5364), an advanced
statistics course, or consent of instructor. In
addition, experience with programming in C or C++ will
be useful to perform assignments and projects.
- Course Materials:
This course does not have a
Readings will consist of
chapters taken from a variety of books and papers from
technical conferences and journals.
Course materials will be available from the instructor or
electronically on the course page.
- E-mail and WWW page:
There is a course web page at
http://www-cse.uta.edu/~huber/cse6369 . All
changes and supplementary course materials will be
available from this site. In addition, necessary
changes or important announcements will also be
distributed by e-mail.
- Tentative Office Hours:
Office hours for the course will be held by the
instructor either in ERB 128 or in 522 ERB,
TTh 11:00 - 12:00, and TTh 4:30 - 5:20. Times are subject to change and will be
posted. If for some reason you can not make it to any
of these office hours, please inform the