Computer Science & Engineering Department


CS 479/679 Pattern Recognition (Spring 2016)

  • Meets: MW 1:00pm - 2:15pm (SEM 257)

  • Instructor: Dr. George Bebis

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

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

  • Important Resources
  • Useful software:
  • Challenges

  • Course Description

    This course will introduce the fundamentals of statistical 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 as nearest-neighbor classification and 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

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

    Exams and Assignments

    Grading will be based on two exams, several quizzes, and 4-5 programming assignments and a paper presentation. Details are provided below:

  • Homework problems will be assigned but will NOT be collected for grading. Homework solutions will be made available for each assignment.

  • There will be 2 exams: a midterm and a final. The material covered in the exams will be drawn from the lectures and the homework.

  • There will be several quizzes during the semester which will be announced at least one class period in advance.

  • There will be 4-5 programming assignments which will be done in groups of two students. For each assignment, the group would need to turn in a report; details will be provided for each assignment.

  • Graduate students will be required to present a research paper to the rest of the class. Each presentation should be professional as if it was presented in a formal conference (i.e., slides/projector). Students can choose a paper from those listed on the course’s webpage or can suggest other papers to the instructor for possible presentation. Presentations should be 20 minutes long and will take place at the end of the semester.

    Course Policies

  • Lecture slides, assignments, and other useful information will be posted on the course.s web page.

  • Regular attendance is highly recommended. If you miss a class, you are responsible for all material covered or assigned in class.

  • A missed exam may be made up only if it was missed due to an extreme emergency.

  • No late assignments will be accepted unless there is an extreme emergency.

  • No incomplete grades (INC) will be given in this course unless there is an extreme emergency.

    Syllabus


    Sample Exam


    Lectures and Reading Assignments


    Homework

    Programming Assignments



    Student Presentations

  • Hongda Tian et al., Single Image Smoke Detection, ACCV, 2014. (Jeff Soriano - April 13, 2016)
  • Paul Blondel et al., Human Detection in Uncluttered Environments: from Ground to UAV View (Seyed Pourya Hoseini Alinodehi - April 18, 2016)
  • Faisal Ahmed et al., Classification of crops and weeds from digital images: A support vector machine approach, Crop Protection, 2012. (Jiwan Bhandari - April 18, 2016)
  • Tom Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, 2006. (Nathan Wiseman - April 20, 2016)
  • Hidenori Maruta et al., A Novel Smoke Detection Method Using Support Vector Machine, IEEE Tencon 2010. (Tuan Dzung Le - April 20, 2016)
  • Sebastian Haug et al., Plant Classification System for Crop /Weed Discrimination without Segmentation, Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on. (Chad Adams - April 20, 2016)
  • Xi Huang et al., An Intelligent Multifeature Statistical Approach for the Discrimination of Driving Conditions of a Hybrid Electric Vehicle, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 12, NO. 2, JUNE 2011. (Yuan Sun - April 25, 2016)
  • Jose Bins and Bruce Draper, Feature Selection from Huge Feature Sets, ICCV 2001. (Ahmet Aksoy - April 25, 2016)


    Papers for Student Presentations




    Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
    Page created and maintained by: Dr. George Bebis (bebis@cs.unr.edu)