ECE662 Pattern Recognition and Decision Making Processes

Fall 2014

Course Information

  • Instructor:  Sarah Koskie
  • Email:
  • Lectures:   TR 7:30–8:45 pm in LD 030
  • Office Hours:   TR 5:45–7:00 pm in SL 164F or by appointment
  • Textbook: Introduction to Statistical Pattern Recognition, 2nd edition, by Keinosuke Fukunaga, Academic Press, 1990.
  • Useful References:
    • Pattern Classification, 2nd edition, by Richard O. Duda, Peter E. Hart, and David G. Stork, Wiley-Interscience, 2000.
    • Statistical Pattern Recognition, 3rd edition, by Andrew R. Webb, and Keith D. Copsey, Wiley-Interscience, 2011. The IUPUI library has an electronic copy of this book, which is outstanding, but is more of a reference book than a textbook. To access it, use IUCAT to search for the book, select the entry that includes the Online Resource icon, and select the link for [Columbus, IUPUI].

  • 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)
      • Mathematical problem formulation
      • Review of probability theory and linear algebra
    2. Pattern recognition and learning machines
      • Bayes classification (Week 2)
      • Parametric classifier design (Week 3, 4)
      • Nonparametric design (Weeks 5, 6)
      • Estimation of classifiability (Weeks 7)
      • Classifier evaluation (Week 8)
    3. Data Analysis
      • Feature extraction for signal representation (Week 9)
      • Feature extraction for classification (Weeks 10)
      • Clustering (Week 11)
      • Modeling and validity tests (Week 12)
    4. Machine Learning
      • Formulation of the learning problem (Week 13)
      • Hypotheses and VC-dimension (Week 14)
      • The linear model and learning principles (Week 15)
  • Lecture Notes   (Updated August 13, 2013)

  • Syllabus