Unless otherwise specified, questions have unit value. The total value of the
assignments from each week will vary substantively.
Recall that assignments are graded rather loosely on effort, and that 3/4 of
the total marks (1/2 for ugrads) over all assignments over all weeks
represents 100%. This policy is in place partly to allow for error in the
grading approach which, by necessity, is somewhat subjective, and needs to be
done somewhat superficially. It is recommended (and requested) that you try to
overshoot the 3/4 requirement, rather than worry about the details of how the
grading is done.
Problems denoted EXTRA can be substituted for other problems, or done in
addition, but they do not count towards the computation of the 3/4
requirement. They may be discussed in class depending on time and interest.
They are problems that I think might be useful, and likely be assigned if we
had more time per chapter.
Sometimes you will explicitly have to choose some of your own problems. Even
when this is not the case, you can substitute some problems in the book for
non-programming assignments if they appear more helpful to you. For now, limit
the number of substitutions to 50% of what you hand in. This parameter may be
increased or decreased as we go on.
You are encouraged to discuss the problems with your peers, but I would like
individual final submissions demonstrating effort and understanding of what
was done. If you end up working closely with someone on a problem set, make a
note on your submission saying who it was. For programming assignments, each
person should turn in their own work.
Since this is graduate level research course that is graded predominately on
effort, I am confident that there will not be any problems with academic
honesty. However, do note that non-negligible deviations are often
surprisingly easy to spot, and can be verified by discussing the submitted
solutions with the student.
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Problems for Week 12.
Total value is 8.
Due Thursday.
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VALUE = 8
Take a look at the material here.
After doing the extra reading ("a practical guide") listed on the schedule
page, build an SVM for the face data. Try different kernels. Report results.
Try at least one more data set. Either one of your own, or chosen from this page.
Try to fine one where the kernel makes a big difference. Be aware of scaling
issues (see the guide). You may want to consider why scaling is important.
Also, make sure that you make good use of the cross validation option.
Report on your experience.