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 Announcements

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.

• 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

• 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:

• 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.