|Instructor: Prof. Richard Zanibbi (web page; rxzvcs at rit dot edu )
Office Hrs: W 1:30-2:30pm, F 12:30-2:30pm
Lectures: MWF 2:30-3:25pm, GOL-3445
An introduction to pattern classification and structural pattern recognition. Topics include Bayesian decision theory, evaluation, clustering, feature selection, classification methods (including linear classifiers, nearest-neighbor rules, support vector machines, and neural networks), classifier combination, and recognizing structures (e.g. using HMMs and SCFGs). Students will present current research papers and complete programming projects such as optical character recognizers. (Pre-requisites: CSCI-331, CSCI-630 or permission of instructor).
- Students will understand Bayesian Decision Theory, the canonical classifier model, and how different classification methods define decision boundaries. Evaluation: Quizzes, assignments, and projects
- Students will be able to apply performance evaluation methods for pattern recognition. Evaluation: Projects
- Students will be able to select appropriate techniques for addressing recognition problems. Evaluation: Quizzes, assignments, and projects
- Students will be able to implement basic pattern recognition algorithms. Evaluation: Assignments and projects
- Students will be able to summarize current pattern recognition research verbally and in writing. Evaluation: Quizzes, assignments and research paper presentations
Lecture. This is an advanced graduate course, which will cover a wide variety of topics, some being complex and/or counter-intuitive. Students should raise their hands to ask a question whenever something is unclear, they want to check their understanding, or have an idea to share. Sometimes the instructor will not call on the student right away, in the interest of covering material and making sure that the course progresses at a reasonable pace. Students are always welcome to send questions over email or talk to the instructor during office hours (see top of page).
Grading. For full points, deliverables in the course including question answers, code, presentations and write-ups must be:
- Correct and complete (all parts/aspects of the question are covered).
- Jusitified: if an assignment or test question asks for an explanation or justification, it must be provided for full points.
- Clearly written: answers/reports should be written with care and attention to language, and provide the context needed to understand the answer with a reasonable effort. Note that the goal here is clarity, not complexity. Make your answer understood in simple terms wherever possible.
- Provided in the requested format. For example, files are submitted in the correct format, a question that asks for a written description is not a bulleted list, etc.
Late Policy and Grade Revision
Late submissions may be submitted at most two days late, with a 10% grade penalty each day. After 48 hours, late submissions will not be accepted. Note that the MyCourses dropbox for assignments will close 48 hours after the due date (i.e. 48 hours after the deadline).
Students may request that the instructor reconsider an assigned grade up to one week after a grade is assigned. After that time, the assigned grade will stand.
Normally I will reply to emails within 24 hours during the work-week, and on Monday for emails received over the weekend.
Assignments and Projects
All assignments will be completed individually. Assignments will be given out one week before they are due. They will include some combination of written questions and small programming tasks or experiments.
There will be a three-part course project, in which we will incrementally construct a complete end-to-end system for recognizing mathematical expressions:
- Classifier ('OCR' for isolated math symbols)
- Segmenter & Classifier ('OCR' for math expressions)
- Parser (recognized symbols and symbol layout in a math expression)
Projects will be assigned three weeks before they are due. Depending on the number of students in the course, projects will be completed either individually or in groups of two, so that there will be enough time in class for discussion from the various teams, and group presentations at the end of the semester.
Student groups will present their system and plans for completing Project 3 (parsing) during the last week of classes.
Research Paper Presentations
A central part of the course is the presentation and discussion of research papers. The instructor will provide sources for papers one week before a presentation is given. Papers will be presented on classification, segmentation/clustering and parsing. Presentations will be given by groups of two or three students. The instructor will try to provide time for discussion of the papers presented in-class. Presentations will be short, between 5 and 10 minutes, depending upon the number of students presenting. Presentations will be graded based on their technical content and clarity.
Disability Services Office
If you have special needs for seating, tests, note-taking services or other matters due to a disability, please contact the Disability Services Office (www.rit.edu/dso). If you receive approval for accomodation within the course, please contact me as soon as possible so that we can make the necessary arrangements.
Help with Mathematics
There is a significant amount of mathematical content in the course. If you find that you are challenged by notation or mathematical concepts used in the course, please make use of the instructor's office hours, or email the instructor to set up an appointment. You will probably find that other students in the course are having similar challenges, and many students find it helpful to meet in small groups from time to time to talk about the mathematical models used in the course.
If you need additional help after consulting the instructor and your classmates, you are encouraged to make use of the RIT Academic Support Center, which has a drop-in center and a number of other useful resources.
Component Weight Assignments (4) 20% Research Presentations (3) 25% Projects (3 (15/15/20) + Group Presentation (5)) 55%
CS Common Course Policies Include:
- Rescheduling an Exam (n/a)
- Course Withdrawal (before end of Week 12)
- Disability Services
- Academic Integrity