Teams: Assignment can be completed by teams of two or three students. No additional credit
for working individually.
Submission: Submit a PDF ﬁle to Sakai. For the programming questions, submit your code in
a compressed ﬁle. DO NOT submit documents in Word, raw text, images, etc. One submission
per team is enough. Be sure to submit before the deadline (see Sakai). You have unlimited
submission, partial submissions are encourage! Code submitted must run on ilab machines in
order to the be graded.
L AT EX: Extra credit (10%) for submitting answers using L AT EX. If you choose to do this, submit all
ﬁles (*.tex) as a separate compressed ﬁle.
Plagiarism: Each team must implement and answer questions independently. Indicate any ex-
ternal sources used for your submission. If plagiarism is detected, the assignment will receive 0
No coding in this assignment.
Acknowledgement: This project is based on the one created by Dan Klein and John DeNero that
was given as part of the programming assignments of Berkeley’s CS188 course.
In this project, you will design three classiﬁers: a naive Bayes classiﬁer, a perceptron classiﬁer
and a classiﬁer of your choice. You will test your classiﬁers on two image data sets: a set of
scanned handwritten digit images and a set of face images in which edges have already been
detected. Even with simple features, your classiﬁers will be able to do quite well on these tasks
when given enough training data.
Optical character recognition (OCR) is the task of extracting text from image sources. The ﬁrst
data set on which you will run your classiﬁers is a collection of handwritten numerical digits (0-
9). This is a very commercially useful technology, similar to the technique used by the US post
ofﬁce to route mail by zip codes. There are systems that can perform with over 99% classiﬁcation
accuracy (see LeNet-5 for an example system in action).
Face detection is the task of localizing faces within video or still images. The faces can
be at any location and vary in size. There are many applications for face detection, including
human computer interaction and surveillance. You will attempt a simpliﬁed face detection task in
which your system is presented with an image that has been pre-processed by an edge detection
algorithm. The task is to determine whether the edge image is a face or not.
Please refer to http://inst.eecs.berkeley.edu/~cs188/sp11/projects/classification/
classification.html for a brief description of the Perceptron and Naive Bayes classiﬁers.
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