CS591Q/CS791V - Pattern Recognition
 Spring 2008
West Virginia University

  • Instructor/Office Hrs
  • Lecture Details
  • Textbook
  • Suggested Prerequisites
  • Course Description
  • Course Topics
  • Grading
  • Grading Policy
    		•

    Instructor and Office Hours:

    Dr. Arun Ross (arun.ross at mail.wvu.edu)
    Office: 751 ESB
    Office hours: TBA

    Lecture Details:

    Time: Tuesday and Thursday, 8:00-9:15a
    Room: ESB-E 801

    Required Textbook:

    Pattern Classification by Duda, Hart and Stork, 2nd edition, ISBN: 0-471-05669-3.

    Suggested Reading Material:
  • Bishop, "Pattern Recognition and Machine Learning".
  • Fukunaga, "Introduction to Statistical Pattern Recognition".
  • Pavlidis, "Structural Pattern Recognition".
  • Gonzalez and Wintz, "Syntactic Pattern Recognition".
  • Devijver and Kittler, "Pattern Recognition: A Statistical Approach".
    • Suggested Prerequisites:
    STAT462, MATH343, or equivalent.
    An undergraduate level understanding of probability, statistics and linear algebra is assumed. A basic knowledge of Matlab is essential.

    Course Description:

    This course will introduce a graduate audience to salient topics in statistical pattern recognition. It will begin by discussing concepts in Bayesian decision theory, Bayesian learning and density estimation. Next, the theory behind linear discriminant functions, multilayer neural networks, support vector machines and unsupervised learning will be presented. Topics in dimensionality reduction, boosting and bagging will also be visited. The project component of this course will test the student's ability to design and evaluate classifiers on real-world datasets.

    Course Topics:

    Click here to view the list of topics that will be covered in this course.

    Grading:

    The tentative weight associated with each grading component is as follows:

  • Homework - 30%
  • Quiz - 15%
  • Midterm exam - 20%
  • Project - 15%
  • Final exam - 20%

    Final grades will be assigned based on the following scale:
  • 90 and above: A
  • 80 - 89: B
  • 65 - 79: C
  • 50 - 64: D
  • 49 and below: F

    • Grading Policy:

  • Homeworks have to be turned in before lecture begins on the due date.
  • No make-up for quizzes.
  • Make-up for exams will be issued only under exceptional circumstances provided prior arrangements are made with the instructor.
  • Instructor reserves the right to deny requests for make-up exams.

    • Homework:

  • Homework 1. Due on 7 Feb (Thu).
  • Homework 2. Due on 6 Mar (Thu).
  • Homework 3. Due on 3 Apr (Thu).
  • Homework 4. Due on 24 Apr (Thu).
  • Homework 5 (Bonus). Due on 2 May (Fri).

    • Practice Tests:

  • Practice Quiz 1. Posted on 26 Feb (Tue).
  • Practice Final Exam. Posted on 3 May (Sat).

    • Project:

  • Final Project. Due on 9 May (Fri), 11:59pm.

    • Resources:

    Matlab Tutorial:
  • MathWorks - Matlab Tutorial
  • A Matlab Primer - Kermit Sigmon
    • Datasets:
  • MNIST database of handwritten digits
  • The UCI Machine Learning Repository
    • Software:
  • Weka 3: Data Mining Software in Java
  • PRTools Toolbox: Matlab-based toolbox for Pattern Recognition
  • Statistical Pattern Recognition Toolbox
    • Papers:
  • A. K. Jain, R. P. W. Duin, and J. Mao, "Statistical Pattern Recognition: A Review," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, Jan. 2000, pp. 4-37.
    • Other Links:
  • Andrew Moore's Data Mining Tutorials
  • Pattern Recognition Homepage

    • "West Virginia is committed to social justice. I concur with that commitment and expect to maintain a positive learning environment based upon open communication, mutual respect, and nondiscrimination. Our University does not discriminate on the basis of race, sex, age, disability, veteran status, religion, sexual orientation, color or national origin. Any suggestions as to how to further such a positive and open environment in this class will be appreciated and given serious consideration.

    If you are a person with a disability and anticipate needing any type of accommodation in order to participate in this class, please advise me and make appropriate arrangement with Disability Services (293-6700)."