Basic knowledge of probability and linear algebra; data structures, algorithms; programming experience.  Previous experience with image processing will be useful but is not assumed. 

Assignments will consist largely of Matlab programming problems.  There will be a warm-up assignment to get familiar with basic Matlab commands, and examples during early class sessions.  We will recommend useful functions to check out per assignment.  Students are expected to practice and pick up Matlab on their own in order to complete the assignments.  The instructor and TA are happy to help with Matlab issues during office hours. 

If you are unsure if your background is a good match for this course, please come talk to me.

Problem sets:  Problem sets will be given approximately every two weeks, and will involve a combination of concept questions and programming problems.  The programming problems will provide hands-on experience working with techniques covered in or related to the lectures.  All code and written responses must be completed individually.  Most problem sets will take significant time to complete.  Please start early, and come see us for help if needed.


Due dates:  All problem sets are to be submitted by 11:59 PM on the day they are due unless otherwise noted on the assignment itself.  Deadlines are firm.  We will use the “turnin” program timestamp to determine time of submission.  Anything from 1 minute to 24 hours is one day late (i.e., a timestamp of 12:00 AM or later the next day is one day late).  The instructions in each problem set will designate which parts to submit electronically and which, if any, to submit via hardcopy.  Any hardcopy portions should be submitted in class the day of the deadline (if class falls on the day it's due) or else left in the dropbox in PAI 5.38.  Please always use a clear cover page with your name and CS376, so it's easy to find in the dropbox.


Late assignment policy:  Throughout the term you have an allowance of three free late days for assignment turn-ins, meaning you can accrue up to three days in late assignments with no penalty.  For example, you could turn in one assignment three days late, or three assignments each one day late.  Once you have used all your free late days, a late assignment will not be accepted.  Please plan ahead so you can spend your late days wisely.  In particular, note that we expect you will find the earlier assignments easier than those later in the course.   Late days are timed as the assignments; we’ll count a full additional day as having passed for submissions 1 minute to 24 hours late. 

If you use any late days on an assignment:

• notify the TA via email once you submit it on turnin.
• send the TA a prepared pdf of all content you would have submitted for the hardcopy if submitting at the deadline, so she can print it. [updated 2/11/11]
  

Exams:  There is an in-class midterm and a comprehensive final exam. 


Participation/attendance:  Regular attendance is expected.  If for whatever reason you are absent, it is your responsibility to find out what you missed that day.  Note that attendance does factor into the final grade. 


General responsibilities:  Beyond the above, your responsibilities in the class are:

• Come to lecture on time.
• Check the class webpage for assignment files, notes, announcements etc.
• Complete the readings prior to lecture.  The reading assignments listed on the schedule should be read before the associated class lecture.
• Please do not use a laptop, cell phone, etc. during class.
• Please read and follow the UTCS code of conduct.

Grades will be determined as follows.  You can check your current grades online.

• Problem sets (50%)
• Midterm exam (20%)
• Final exam (20%)
• Class participation, including attendance (10%)

Midterm exam: Wednesday March 9 (in class)
Last class meeting: Wednesday May 4
Final exam: Monday May 16, 2:00-5:00 PM JGB 2.102

The recommended textbook is


You may also find the following books useful.  Copies are on reserve for members of our class to use at the PCL library. 

• Computer Vision : A Modern Approach, David A. Forsyth and Jean Ponce
• Computer Vision, Linda G. Shapiro and George C. Stockman
• Introductory Techniques for 3-D Computer Vision, Emanuele Trucco and Alessandro Verri.
• Multiple View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman.
• Pattern classification, Richard O. Duda, Peter E. Hart, and David G. Stork
• Pattern Recognition and Machine Learning.  Christopher M. Bishop