Up-to-date information is at http://cse.unl.edu/~sscott/cse970S07
| INSTRUCTOR: |
| Stephen Scott |
| 364 Avery |
| 472-6994 |
| sscott AT cse |
| Office Hours: 12:00-1:30 M 2:00-3:30 R |
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 Textbook:
Pattern Recognition and Machine Learning by Christopher M. Bishop, Springer, 2006. ISBN 0-387-31073-8
Optional Textbook: Learning Bayesian Networks by Richard E. Neapolitan, Prentice Hall, 2004. ISBN 0-13-012534-2
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.
In contrast with previous offerings of this course, there will be no homeworks or project. Instead, the course requirements will be regular attendance (if you must miss a class session due to personal emergency, conference attendance, etc. contact me), a major written (wiki-based) and oral (in-class lecture) presentation of course material, substantive edits of other students' written presentations of course material (i.e., edit their wikis), and at the end of the term a presentation of your own research, tied as much as possible to the course content.
The above items will be weighted as follows:
| attendance: 10% | your wiki: 30% | lecture: 30% | editing others' wikis: 15% | research presentation: 15% |
In computing your letter grade I will start with the following base scale, where s is your final score:
| s ≥ 90% Þ A | 80% ≤ s < 90% Þ B | 70% ≤ s < 80% Þ C | 60% ≤ s < 70% Þ D | s < 60% Þ F |
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.