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Department of Computer Science
4005-759-01 (Calendar Description) |
Instructor: Richard Zanibbi, Office hours: 2-3:50pm Tuesdays and Thursdays, Room 70-3551 (Golisano College)
Lectures: 10-11:50am Tuesdays and Thursdays, Room 70-2590 (Golisano College)
An introduction to pattern classification and structural pattern recognition. Topics include: feature extraction, Bayesian decision theory, nearest-neighbor rules, clustering, support vector machines, neural networks, classifier combination, and syntactic pattern recognition techniques such as stochastic context-free grammars. The course is part lecture and part seminar: students will present some course material to the class as well as complete and present a research paper. In addition, programming assignments will provide students with practical experience in constructing pattern recognition systems such as optical character recognizers (OCR). Class hours: 4, Credit: 4
4003-455 (Artificial Intelligence) or 4005-750 (Introduction to Artificial Intelligence), or Permission of the instructor.
At the end of this course, students will be able to:
Course Web Page: http://www.cs.rit.edu/~rlaz/PatternRecognition/
Required Text: Pattern Classification (2nd Edition) by R.O.
Duda, P. E. Hart and D. Stork, Wiley 2001 [ Web Page ]
Additional sources will include research papers, books (some available from the Wallace Library) and others. A list is provided within the course web pages at: http://www.cs.rit.edu/~rlaz/PatternRecognition/Resources.html. Sources provided include materials on writing research papers for Computer Science, which students are strongly encouraged to consult.
Students will be evaluated based on (roughly 2-3) in-class group presentations of course material, a course project (completed in teams), a research paper (completed individually: includes an outline, rough draft, final version, and presentation), and midterm and final examinations.
| 20% | Seminar Presentations |
| 20% | Project (Final report: 15%, Proposal: 5%) |
| 20% | Research Paper (Final paper and presentation: 15%, Proposal: 5%) |
| 15% | Midterm Examination |
| 25% | Final Examination |
Please note that this schedule may change over the course of the quarter: look under the "Schedule & Slides" page on the course web pages for the current schedule.
| Week | Topics | Assignments
| 1 | Overview |
| 2 | Bayesian Decision Theory |
| 3 | Feature Extraction and Bayesian Classification | Thurs: Paper proposal due
| 4 | Nonparametric Methods (e.g. nearest neighbor) |
| 5 | Linear and Quadratic Classifiers, SVMs | Thurs: Project proposal due
| 6 | Neural Networks and Parameter Estimation | Thurs: Midterm (1 hr)
| 7 | Classifier Combination | Thurs: Last day to submit paper draft
| 8 | Clustering |
| 9 | Syntactic Pattern Recognition | Thurs: Project report due
| 10 | Project Summary, Research Paper Presentations | Thurs: Final paper due
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Late submissions are penalized 10% (1 letter grade) per day, for up to three days. After three days, late submissions will not be accepted.
Exams will only be rescheduled in the case of difficult situations for which there is formal documentation (e.g. a doctor's note). See the instructor as soon as possible if you encounter scheduling or other issues regarding the exams.
Students may discuss assignments and projects with others, but submitted work (papers and code) must be created independently by each student or group, and not copied from another student or other source.