Up-to-date information is at http://csce.unl.edu/~sscott/teach/Classes/cse478F04
INSTRUCTOR: |
Stephen Scott |
364 Avery |
472-6994 |
sscott at cse |
Office Hours: 9:30-10:30 M, 2:30-3:30 T, 10:00-11:00 R |
TA: |
Xuli Liu |
501 Building, Room 5.3 |
472-5029 |
xuliu at cse |
Office Hours: 1:30-2:30 M, 9:30-10:30 W, 1:30-2:30 R |
PREREQUISITES: CSCE 310 (Data Structures & Algorithms) required, STAT 380/880 (Prob. and Stats.) helpful
TIME: 12:30-1:20 Monday, Wednesday, Friday
CLASSROOM: Avery 112
TEXTBOOKS:
Required: Machine Learning, by Tom Mitchell. McGraw-Hill, 1997, ISBN 0070428077. [Book's web page includes errata.]
Useful for learning about support vector machines, but not required: An Introduction to Support Vector Machines, by Nello Cristianini and John Shawe-Taylor. Cambridge University Press, 2000. ISBN 0-521-78019-5.
These books are on reserve at Love Library.
You may consult each other for assistance on the homework, but you must write up your results in your own words and indicate whom you consulted. 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 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 as homework solutions.
EXAMS
There will be no exams in this course except for the
prerequisite test, which
will evaluate your understanding of this course's necessary background material
as well as your writing ability.
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.; the homework late penalty applies here as well) 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. See a small set of sample topic summaries
to use as guides in writing your own.
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 5-6 other students' submissions of one topic summary (5 for 478, 6 for 878). For that topic summary, you will receive a perfect score so long as you grade according to the above guidelines (grading will be done by filling out a web form). So if you grade students' submissions for e.g. the topic summary on decision trees, 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
machine learning, or (3) a small (but nontrivial) amount of original
research related to machine learning. You may work on these
projects individually or in small groups, though if you work in a group,
my expectations will be much 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 (December 6-10), and if necessary, during the week prior to Dead Week (November 29-December 3). You will submit your written report to me no later than 11:59 p.m. on December 10 (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 (around October 6) 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.
DUMB QUESTION ASKER
Each lecture, I will select a student who will be required to
ask three questions during that lecture.
During your assigned lecture,
you may ask any question concerning any course material that you do not
understand. If you completely understand the material, then you may
ask any question that you feel would help the other students (even if
you already know the answer yourself). If you cannot think of a
question to help the rest of the class, then any "dumb question"
(e.g. "Who will win the game on Saturday?" or "Who gave you that
haircut? Beavers?") will suffice. But
you must ask three questions during your assigned lecture.
GRADING
The above items will be weighted as follows:
prereq test: 5% | hwks: 30% | proj. report: 25% | proj. presentation: 15% | topic summaries: 15% | subjective: 10% |
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 |
In general, students registered for CSCE 878 will be graded more stringently on everything and will have more problems to solve on the homework assignments.
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; please report problems to sscott.