Syllabus for CSCE 970 (Spring 2009)

Up-to-date information is at http://cse.unl.edu/~sscott/cse970S09

CONTENTS

• COURSE INFORMATION
• COURSE OBJECTIVES
• COURSE ELEMENTS

COURSE INFORMATION

PREREQUISITES: Math 314 (Linear Algebra), CSCE 310 (Data Structures & Algorithms), STAT 380 (Prob. and Stats.)

TIME: 10:30–11:20 MWF

CLASSROOM: Avery 112

TEXTBOOKS:

• REQUIRED: Learning Bayesian Networks by Richard E. Neapolitan, Prentice Hall, 2004. ISBN 0-13-012534-2
• NOT REQUIRED, BUT USEFUL FOR LEARNING ABOUT HMMs: Biological Sequence Analysis by Richard Durbin et al., Cambridge, 1998. ISBN 0-521-62971-3

COURSE OBJECTIVES

In this course you will learn several of the fundamentals and current trends in pattern recognition, with an emphasis on so-called "graphical models" such as Bayesian networks and hidden Markov models. These models are rigorously justified, provide a distributed knowledge representation, and are as understandable as a rule base. They deal particularly well with uncertainty, and they can be manually generated by consultation of an expert, or inductively built by machine learning. These approaches are used for modelling knowledge in gene regulatory networks, medicine, engineering, text analysis, image processing, data fusion, and decision support systems. A sampling of specific applications includes (1) automatic interpolation of raw data from interplanetary probes and deep space explorations; (2) modeling pilot-aircraft interactions; (3) the "Office Assistant" that was introduced in the Office 95 suite of desktop products; and (4) modeling of DNA and protein sequences.

COURSE ELEMENTS

HOMEWORK
There will be 2–4 homework assignments, each due by 11:59 p.m. on its due date. (The 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 may include non-computer-based problems (i.e. theoretical exercises) and computer-based problems (i.e. implementations). 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. 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. ) to write your homework submissions, and you must submit your reports electronically in pdf format (see "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.

WIKI
This semester you will be assigned a topic related to the course material. You will prepare a detailed written (wiki-based) presentation of this topic. You will also make substantive edits of three other students' wikis. More details will come later. The wikis will be accessed via the CSCE 970 (Spring 2009) main wiki page. You can see examples of wikis on different topics on the 2007 wiki page. Your wiki is due by the end of Dead Week (May 1) and your edits to others' wikis are due the Tuesday of Finals Week (May 5). Of course, you are encouraged to complete them much earlier.

PROJECT
In this course you will do a substantial project on a topic that is related to pattern recognition, preferably to the specific content of the course. 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 or (3) a small (but nontrivial) amount of original research. 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 27–May 1) and Finals Week (May 4–8). You will submit your written report to me no later than 11:59 p.m. on May 3 (the Sunday before Finals Week). In accordance with UNL policies, you have now been informed in writing of the nature and scope of this project prior to the eighth week of classes.

By 11:59 p.m. on February 22, you will submit a 1–3 paragraph proposal on your project. 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:

In computing your letter grade I will start with the following base scale, where s is your final score:

You will receive a "+" with your grade if the last digit of your score is ≥ 7, and a "–" if the last digit is < 3. 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 and your level of class participation. 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 UNL 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.