Dept. of Systems Design Engineering
Faculty of Engineering
University of Waterloo
Canada N2L 3G1
Tel: (519) 888-4567 x84970
FAX: (519) 746-4791
Your handout should be at a high level, be understandable to other students in the class. Ideally, you should include a few references for students who might want to learn more on the topic.
I have here a couple of example handouts, just to give you a rough sense of
what others have done. However your handout does *not* need to look like
one of these:
Handout #1: side 1, side 2. Handout #2: side 1, side 2. Handout #3: only one side
Your project needs to represent your own work, cannot be copied from previous work (such as a thesis proposal or comprehensive exam), must be properly cited, and cannot have any material copied (plagiarism). Copying even single sentences or phrases is unacceptable.
You can submit a hardcopy in my mail slot or under my door (DC2643).
You can also email me an electronic copy, but which MUST be in PDF format
(no Word files!). So that I can sort and find your email easily, your email
must have "675" in the subject line,
e.g., "Subject: SD675 Project"
P. Fieguth: Office Hours, Mondays 12-1pm, DC-2643
2009 Course Syllabus
This course is an advanced version of SY DE 372 - Introduction to Pattern Recognition. For those students lacking a background course in pattern recognition, it is recommended that you attend some or all of the SD372 lectures. The material discussed in SD675 will overlap only relatively little with that from SD372.
The course starts with a brief summary of SY DE 372: probabalistic classifiers, discriminant functions, unlabeled clustering, and feature extraction.
More advanced topics will include some information theory (as it pertains to feature extraction), statistical estimation and error analysis (relating to parameter estimation), neural networks, self-organizing maps, and syntactical/grammatical pattern recognition.
The course grade is based on a few computer labs, one or two problem sets, and a term project.
2009 Course Reference List
Assignment and Lab Information
I will keep a list of current and past assignments here:
The data set assign2.mat for this assignment is located here Right-click to download, save to disk, and open in Matlab.
To keep the results consistent from student to student, I am giving you the data points for this assignment instead of you generating them yourselves. The data set is assign3.mat; if, for some reason, you are using a very old version of Matlab, then you might try assign3_v4.mat. Right-click to download, save to disk, and open in Matlab.
Students may wish to numerically evaluate expressions involving Q(). The Matlab routine for Q() is here.
This lab assignment is on Boosting. The data set is the same one as Case 4 from the previous two labs, and is available here.
This assignment has 5 short questions, no big programming, a couple of small analytical questions. No data set needed for this lab.
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