CSE 802 - Pattern Recognition and Analysis
Spring 2016
Michigan State University

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

  • Instructor and Office Hours:

    Dr. Arun Ross (rossarun at cse.msu.edu)
    Office: 3142 Engineering Building
    Office hours: 11:00am - 12:00pm on Wednesday, or by appointment

    Lecture Details:

    Time: Monday and Wednesday, 3:00pm - 4:20pm
    Room: 226 Erickson Hall

    Required Textbook:

    Pattern Classification (2nd Edition), ISBN: 9-780-471-056-690.

    Suggested Reading Material:
  • Bishop, "Pattern Recognition and Machine Learning".
  • Fukunaga, "Introduction to Statistical Pattern Recognition".
  • Pavlidis, "Structural Pattern Recognition".
  • Gonzalez and Thomason, "Syntactic Pattern Recognition".
  • Devijver and Kittler, "Pattern Recognition: A Statistical Approach".
  • Prerequisites:
    CSE 232, MTH 314, and STT 441, or equivalent courses.
    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. These include concepts in Bayesian decision theory, parametric and non-parametric density estimation schemes, linear discriminant functions, neural networks and unsupervised clustering. Topics in dimensionality reduction and deep learningwill also be visited. The project component of this course will test the student's ability to design and evaluate classifiers on datasets.

    Course Topics:

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


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

  • Homework - 36%
  • Quiz - 20%
  • Midterm exam - 14%
  • Project - 15%
  • Final exam - 15%

    Final grades will be assigned based on the following scale:
  • 90 and above: 4.0
  • 85 - 89: 3.5
  • 80 - 84: 3.0
  • 70 - 79: 2.5
  • 60 - 69: 2.0
  • Below 60: 1.0
  • 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 8 Feb (Mon).
  • Homework 2. Due on 21 Mar (Mon).
  • Homework 3. Due on 13 Apr (Wed).
  • Homework 4. Due on 29 Apr (Fri).
  • Resources:

    Lecture updates:
  • Topics covered so far
  • Refresher on Matrices, Vectors, Probability:
  • R. C. Gonzales, R. E. Woods, "Brief Review of Matrices, Vectors, Probability and Random Variables", 2002. [Downloaded from here]
  • Matlab Tutorial:
  • A Matlab Primer - Kermit Sigmon
  • Datasets:
  • FunSpec Dataset
  • MNIST database of handwritten digits
  • Public Datasets on Amazon Web Services (AWS)
  • The UCI Machine Learning Repository
  • National Space Science Data Center
  • Enron Email Dataset
  • Sam Roweis' Data for Matlab Hackers
  • Software:
  • Weka 3: Data Mining Software in Java
  • Deep Learning Software
  • 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 Trans on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, Jan. 2000, pp. 4-37.
  • Other Links:
  • Andrew Moore's Data Mining Tutorials

  • Academic Integrity: Article 2.3.3 of the Academic Freedom Report states "The student shares with the faculty the responsibility for maintaining the integrity of scholarship, grades, and professional standards." In addition, the Department of Computer Science and Engineering adheres to the policies on academic honesty as specified in General Student Regulations 1.0, Protection of Scholarship and Grades; the all-University Policy on Integrity of Scholarship and Grades; and Ordinance 17.00, Examinations. (See Spartan Life: Student Handbook and Resource Guide)

    Therefore, unless authorized by your instructor, you are expected to complete all course assignments, including homework, projects, quizzes, tests and exams, without assistance from any source. You are expected to develop original work for this course; therefore, you may not submit course work you completed for another course to satisfy the requirements for this course. Students who violate MSU academic integrity rules may receive a penalty grade, including a failing grade on the assignment or in the course. Contact your instructor if you are unsure about the appropriateness of your course work. (See also the Academic Integrity webpage.)