Advanced Topics in Computational Intelligence (fall 2010):
|Office Hours||TBA, by automated sign up.|
|Time and Place||Friday 9:30-12:00, Gould-Simpson 942|
This course will be an intense research focused seminar course that will
combine computer vision content with significant project work, paper
writing, and presentation. The computer vision content will be chosen to
compliment what is covered in CS 577. (Students who have not taken CS 577
should see "prerequisites" below). Presentation of the content will be
handled largely by students. The instructor will provide short lectures on
research methodology and effective writing and presentation.
Some particulars will have to wait until the first week of classes as they depend on the class size and student interest.
We will develop a collection of topics and classic papers as the class
progresses, using an initial set provided by the instructor as a starting
point. Students will present the material in a tutorial fashion for roughly
half of the overall class time. While all students will study the material,
and topic presenters will assign reading as appropriate, the presenters will
be challenged to teach the material. Lectures will followed by short group
feedback sessions. Example topics (under construction) include:
Much of the course will revolve around student research projects. Research
projects must have some connection with computer vison, but can have
substantive aspects from related fields such as machine learning, graphics,
multimedia, robotics, or cognitive science. We will assume sufficient prior
exposure to computer vision or related fields so that projects can be
started very early in the term. Project selection will be finalized within
the first three weeks. A list of project ideas (under construction) can be
Projects can emerge from that list, students own research, vision lab
research, or needed computer vision work around campus. Projects must
contribute to a research effort or infrastructure, either new or ongoing,
within the vision lab or external to it. Projects that have no use after the
class ends are not suitable for this course.
Many possible projects are "encumbered" in that they are in collaboration with ongoing efforts, and will potentially require sharing of authorship and software products, as well as requirements on how software is developed (e.g., language and platforms). Students will have to take these issues into account when choosing a project.
Group projects are possible, but each student needs to partition off a conceptual piece of the work, and write their own paper. Sharing of material such as introductory text is not permissible --- papers from collaborating projects should cite each other. How the collaboration will work needs to be addressed in the research proposal. Similarly, projects that continue or extend ongoing research is possible, but how this is a separate piece of work may need to be addressed in the proposal.
Sharing of infrastructure (e.g., software, ground truth data) is strongly encouraged, both among collaborating students and in the group as a whole. In fact, contributions to usable research infrastructure will contribute to the project impact marks and/or bonus marks, as well as research karma.
Multiple aspects of the project will undergo "peer review", patterned on grant and research paper reviewing. In addition, project progress will be presented in class. For a tentative schedule of due dates, follow this link.
A previous computer vision course (e.g., CS 577), or research experience in
computer vision or a related areas (e.g., machine learning or graphics), or
permission from the instructor. We will hit the ground
running doing computer vision, and thus it is critical that students have
both the motivation and background to do so. In particular, familiarity with
the following concepts will be assumed and thus need to either be reviewed or
learned in a hurry:
|Required Text||There are no required texts. All readings will be made available. However, much material will follow "Computer Vision: A Modern Approach," by Forsyth and Ponce, which is the recommended general reference this course. The computer vision lab has a few copies of this book as well as other relevant volumes that can be signed out for a few days.|
Grading will be based on performance in the following areas (details subject to change).
A cumulative percentage of 90% guarantees an A, 80% guarantees a B, 70% a C, and 60% a D.
Good attendance is required. If you cannot make class due to travel or sickness,
please let the instructor know, as missed classes can count against the
participation grade. The degree of impact will be significant if there are more
than two unexplained missed classes.
Students will be expected to respect the University of Arizona's academic integrity policy.