Course Description

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 dedicated textbook.
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 . 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 instructor.

Manfred Huber