Up-to-date information is at http://cse.unl.edu/~sscott/teach/Classes/cse978S06
INSTRUCTOR: |
Stephen Scott |
364 Avery |
472-6994 |
sscott AT cse |
Office Hours: M 10:30am-12:00pm, R 12:30-2:00pm, and by appointment |
PREREQUISITES: CSCE 310 (or background in data structures and algorithms) and background in calculus, linear algebra, and probability and statistics. CSCE 478/878 (Machine Learning) or CSCE 970 (Pattern Recognition) is useful, but not required.
TIME: 9:30-10:45 Tuesday, Thursday
CLASSROOM: Avery 112
CSCE COURSE TRACK CLASSIFICATION: Applications track
CREDITS: 3 hours
TEXTBOOKS:
Required: Learning with Kernels by Bernhard Schölkopf and Alexander J. Smola, MIT Press, 2002.
Optional: Convex Optimization by Stephen Boyd and Lieven Vandenberghe, Cambridge University Press, 2004.
Useful, but not required:
The textbook is far too large to completely cover in depth. As such, we will cover a subset of the material. Most of the lectures will be based on material from the book, but when necessary I will supplement it with relevant technical papers.
Unless otherwise indicated on a specific assignment, you may not consult each other for assistance on the homework beyond asking each other for basic clarification on the course material or on basic programming techniques. If you are unsure whether the help you want to ask for from another student is appropriate, consult me first (refer to the CS&E Department's Policy on Academic Integrity for more information). Further, you must write up your results in your own words and indicate whom you consulted, and on what.
You must use some document processing package (e.g. LaTeX) to write your homework submissions, and you must submit your reports electronically in pdf format (see ``Help on Creating pdf Files'' and ``Help with LaTeX, etc.''). You must also write as clearly and concisely as possible. Presentation of your results is as important as the results themselves, and will be heavily weighted in grading. If I cannot understand what you wrote due to poor writing, etc., then I cannot award full credit, even if your answers are correct. Thus I recommend that you ask someone (e.g. a friend or someone from the English Department, but not a student in this class) to proofread your write-ups before you submit them, to check for clarity, typographical errors, etc. If English is not your native language, then I strongly recommend this!
Finally, ensure that all your files (e.g. program code, homework write-ups) are reasonably well-protected. You will be held responsible if someone copies your files and submits them to me as homework solutions.
EXAMS
There will be no exams in this course.
TOPIC SUMMARIES
After we complete each topic in lecture, you will submit a brief (3-5
pages) summary of that topic. This is due one week
(at 11:59 p.m.) after
we finish covering that topic in class (as with the
homeworks, all submissions must be electronic in pdf format).
No collaboration is
allowed! Your summary will be based on the lecture, relevant
readings from the text, and any other supplementary material that I
distribute in class.
Your summary should include at least the following:
(a) an overview of the ``big picture'' of that topic; (b) a description
of what you feel are the most and least interesting results related to
that topic; (c) 2-3 questions on material that you did not understand
from the readings and lectures; (d) 2-3 interesting research ideas
related to this topic; and (e) a detailed description of one subtopic.
This summary must be in your own words!
If you merely copy material from the textbook or the papers, you will
be severely downgraded. Finally, as with the homeworks and projects,
quality of writing and brevity will be heavily weighted in the
grading.
At the end of the semester, I will drop your lowest topic summary grade when computing your final average score on the topic summaries. In addition, each of you will be expected to grade about 8-10 other students' submissions of one topic summary. For this particular topic summary, you will receive a perfect score so long as you grade according to the above guidelines. So if you grade students' submissions for e.g. the topic summary on kernel construction, then you need not submit your own summary on that topic.
PROJECT
In this course you will do a substantial project. This project can be:
(1) a very extensive literature search and summary on a particular
topic, (2) a good implementation and evaluation of a known result in
SVMs, or (3) a small (but nontrivial) amount of original
research related to SVMs. You may work on these
projects individually or in small groups, though if you work in a group,
my expectations will be higher when I grade your project.
You will summarize your project results in a written report and an oral presentation. If your project involves an implementation, then you may be asked to also give a brief demonstration. The written report must use a professional writing style similar to that found in an ACM or IEEE journal, including abstract, introduction, summary of related work, your contribution, references, and an appendix (if necessary). The oral presentation will be to the entire class at the end of the semester during dead week (April 24-28). You will submit your written report to me no later than 11:59 p.m. on April 28 (the last day of dead week). In accordance with UNL dead week policies, you have now been informed in writing of the nature and scope of this project prior to the eighth week of classes.
Later this semester I will set a deadline for submission of 1-3 paragraph proposals on your projects. You must do this in order to get full credit for your project, and you must get my approval on it before starting work on your project. I will provide a list of possible topics later this semester, but you may propose your own topic as well. To be a valid topic, it must go beyond the scope of the course. So your project could be on a topic we did not cover in class at all, or could more deeply explore a topic we covered in class.
GRADING
The above items will be weighted as follows:
hwks: 35% | proj. report: 30% | proj. presentation: 20% | topic summaries: 15% |
In computing your letter grade I will start with the following base scale:
90% = A | 80-89% = B | 70-79% = C | 60-69% = D | 59% = F |
Academic dishonesty of any kind will be dealt with in a manner consistent with the CS&E Department's Policy on Academic Integrity. You are expected to know and abide by this policy.
Last modified 16 August 2011.