Up-to-date information is at http://csce.unl.edu/~sscott/CSCE970
|sscott AT cse|
|Office Hours: 2:00-3:00 T & R, 1:00-2:00 W|
PREREQUISITES: Math 314 (Linear Algebra), CSCE 310 (Data Structures & Algorithms), STAT 380 (Prob. and Stats.)
TIME: 12:30-1:45 Tuesday, Thursday
CLASSROOM: Ferguson 114
Required: Pattern Recognition, by Sergios Theodoridis and Konstantinos Koutroumbas. Academic Press, 1999, ISBN 0-12-686140-4. [Also see the errata page.]
Useful for learning about hidden Markov models, but not required: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, by Richard Durbin, Sean Eddy, Anders Krogh, and Graeme Mitchison. Cambridge University Press, 1998. ISBN 0-521-62971-3.
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.
In this course we will follow the book closely, but when necessary I will supplement it with relevant technical papers. For example, when we discuss new methods in machine learning, I will provide copies of papers summarizing the results.
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. ) 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.
There will be no exams in this course.
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 11:59:59 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 two lowest topic summary grades when computing your final average score on the topic summaries. In addition, each of you will be expected to grade about 5-7 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 nonlinear classifiers, then you need not submit your own summary on that topic.
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 pattern recognition, or (3) a small (but nontrivial) amount of original research related to pattern recognition. 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 (April 28-May 2), and if necessary, during the week prior to dead week (April 21-25). You will submit your written report to me no later than May 2 (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.
DUMB QUESTION ASKER
Each Tuesday, I will select a student who will be required to ask three questions during each lecture of that week. During your assigned lectures, 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?'') will suffice. But you must ask three questions during each of your assigned lectures.
The above items will be weighted as follows:
|hwks: 35%||proj. report: 25%||proj. pres.: 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|
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 AT cse.