Computer Science & Engineering Department


CS 479/679 Pattern Recognition (Spring 2019)

  • Meets: TR 12:00pm - 1:15pm (SEM 261)

  • Instructor: Dr. George Bebis

  • Assistant: Ebrahim Emami (coding questions)

  • Text:
  • Optional Texts (not required):
  • Research

  • PR Journals
  • PR Conferences
  • Useful Mathematics and Statistics resources

  • Important Resources
  • Useful software:

  • Course Description

    This course will introduce the fundamentals of pattern recognition. First, we will focus on generative methods such as those based on Bayes decision theory and related techniques of parameter estimation and density estimation. Next, we will focus on discriminative methods such support vector machines. Methods of pattern recognition are useful in many applications such as information retrieval, data mining, document image analysis and recognition, computational linguistics, forensics, biometrics and bioinformatics. In this course, we will emphasize computer vision applications.


    Course Outline (tentative)

  • Introduction
  • Bayesian Decision Theory
  • Bayesian Networks
  • Maximum Likelihood Estimation
  • Dimensionality Reduction
  • Feature Selection
  • Bayesian Estimation
  • Linear Discriminant Functions
  • Support Vector Machines (SVMs)
  • Expectation-Maximization (EM) Algorithm
  • Non-parametric Estimation
  • Selected Topics


    Course Prerequisites

    CS 202 with a "C" or better; MATH/STAT 352. Credit hours: 3.0

    Exams and Assignments

    Grading will be based on several quizzes, two exams, and 4-5 programming assignments. Graduate students will be required to present a paper to the rest of the class. Homework problems will be assigned on a regular basis but will not be collected for grading. Homework solutions will be made available for each assignment.

    Course Policies

    Lecture slides, assignments, and other useful information will be posted on the this web page. Discussion of the of your work is allowed and encouraged. However, each student should do his/her own work. Assignments which are too similar will receive a zero. No late work will be accepted unless there is an extreme emergency. If you are unable to hand in an assignment by the deadline, you must discuss it with me before the deadline. Both exams will be closed books, closed notes. If you are unable to attend an exam you must inform me in advance. No incomplete grades (INC) will be given in this course and a missed exam may be made up only if it was missed due to an extreme emergency. Regular attendance is highly recommended. If you miss a class, you are responsible for all material covered or assigned in class. You should carefully read the section on Academic Dishonesty found in the UNR Student Handbook. Your continued enrollment in this course implies that you have read it, and that you subscribe to the principles stated therein.


    Syllabus


    Sample Exams


    Lectures and Reading Assignments


    Homework



    Programming Assignments



    Papers for Grad Student Presentations

    Presentation Guidelines