ECE662 Pattern Recognition and Decision Making Processes

Fall 2004

Course Information

• Instructor:  Prof. S. Koskie

• Email:  skoskie@iupui.edu

• Lectures:   MW 5:45–7 pm in SL 165

• Textbook: Introduction to Statistical Pattern Recognition, 2nd edition, by Keinosuke Fukunaga, Academic Press, 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)

• Some Useful Links:

  • IUPUI Campus-Wide Course Policies

  • IUPUI Graduate Office: Student Life and Support

  • IUPUI Library Hours

  • List of Greek Letters and how to write them:    (Thanks to Harry Foundalis!);

• Other Semesters:

  • ECE 662 Pattern Recognition and Decision Making Processes   (Fall, 2017)

  • ECE 662 Pattern Recognition and Decision Making Processes   (Fall, 2014)

  • ECE 662 Pattern Recognition and Decision Making Processes   (Fall, 2013)