ECE662 Pattern Recognition and Decision Making Processes

Fall 2004

Course Information

  • Instructor:  Sarah Koskie

  • Email:  skoskie@iupui.edu

  • Lectures:   MW 5:45–7 pm in SL 165

  • Office Hours:   T 2-4pm in SL 164F or by appointment

  • Textbook: Introduction to Statistical Pattern Recognition by K. Fukunaga, Academic Press, Second edition, 1990.

  • Prerequisites: ECE 302 (Probabilistic Methods in Electrical Engineering) or equivalent

  • Description: Introduction to the basic concepts and various approaches of pattern recognition and decision making process. The topics include various classifier designs, evaluation of classifiability, learning machines, feature extraction and modeling.

  • Tentative Outline:
    1. Introduction (Week 1)
      • A. Problems in the decision making processes
      • B. Mathematical formulation
    2. Pattern recognition and learning machines
      • Review of probability theory and linear algebra (Week 2)
      • Bayes classification (Week 3)
      • Parametric classifier design (Week 4)
      • Nonparametric design (Weeks 5, 6)
      • Estimation of classifiability (Weeks 7, 8)
      • Classifier evaluation (Week 9)
      • Learning algorithms (Week 10)
    3. Data Structure Analysis
      • Feature extraction for signal representation (Week 11)
      • Feature extraction for classification (Weeks 12, 13)
      • Clustering (Week 14)
      • Modeling and validity tests (Week 15)

  • Lecture Notes   (Updated August 13, 2013)

  • Homework Assignments    (Updated November 02, 2004)


Page last modified 08/13/13.