Syllabus for CSCE 990 - Support Vector Machines (Spring 2006)


Up-to-date information is at http://cse.unl.edu/~sscott/teach/Classes/cse978S06


CONTENTS


COURSE INFORMATION

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 last book is not on SVMs, but gives an overview of machine learning in general.) These books will be on reserve at Love Library.


COURSE OBJECTIVES

Support vector machines is a relatively new area of research in machine learning that is very general and powerful and has been successfully applied to problems in biological sequence analysis, text classification, image processing, data mining, and several other areas. In this course you will learn several of the key algorithms and theory that form the core of support vector machines, including the notion of the margin, the design and use of kernels, and the formulation of a learning problem as a convex optimization problem that can be solved optimally. In this course we will review these basic SVM concepts as well as a few advanced topics such as implementation issues, kernel design, the appropriateness of various kernels for different applications, and (time permitting) kernel principal component analysis and one-class classification. At the end of the course, the students will sufficiently understand the fundamentals of SVMs to design and use their own SVM approaches to various problems and to perform basic research in SVMs.

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.


COURSE ELEMENTS

HOMEWORK
There will be 2-4 homework assignments, each due by 11:59 p.m. on its due date. (cse's system clock, which timestamps your submissions, is the official clock for this course. Do not assume that just because the handin program allows you to submit that the deadline has not passed; you are responsible for ensuring that your files are submitted and timestamped before the deadline.) These assignments will include non-computer-based problems (i.e. theoretical exercises) and computer-based problems (i.e. implementations). The former is to help you comprehend the theory and the latter is to help you understand how the theory is used in practice. Late homework submissions will be penalized exponentially: if your submission is m minutes late, your final score will be multiplied by 2-m/60. Thus you will lose 16% of your points if you submit 15 minutes late, 29.3% of your points if you submit 30 minutes late, 50% if you submit 60 minutes late, etc. (amount of lateness is measured by the time stamp given by cse's web-based handin, which is the only means you are to use to submit assignments). Thus unless you are making very significant improvements to your submission, it is better to submit a partially completed homework assignment than a late one. If you have a valid excuse for a late submission (e.g. illness), contact me as soon as possible.

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

You will receive a ``+'' with your grade if the last digit of your score is a 7 or higher, and a ``-'' if the last digit is a 3 or lower. I will scale up from this base scale if necessary. So if you get an 87% in this course you are guaranteed a B+ (similarly, an 82% guarantees a B-), but your grade might be higher depending on your performance relative to the rest of the class. Note that a C is required to pass the course; a C- is insufficient.

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.