CmpE 621 Pattern Recognition

Fall Semester 2000-2001

Ethem Alpaydin

Topics covered by lectures

Catalog Data

Bayes Decision Theory. Parametric and Nonparametric Methods. Linear Discriminant Functions. Higher Order Discriminants with Emphasis on Artificial Neural Network Based Learning Methods. Unsupervised Learning and Clustering. Case study: Vision.

Reference Books

  • Duda, R., Hart, P. (1973) Pattern Classification and Scene Analysis, Wiley.
  • Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition, 2nd Edition, Academic Press.
  • McLachlan, G. (1992) Discriminant Analysis and Statistical Pattern Recognition, Wiley.
  • Schalkoff, R. (1992) Pattern Recognition: Statistical, Structural, and Neural Approaches, Wiley.
  • Instructor

    Dr Ethem Alpaydin, Associate Professor. Department of Computer Engineering, Bogazici University


    To introduce the student to the problems related to pattern recognition and discuss how solutions may be attempted using statistical techniques. This course is followed by and is a prerequisite for CmpE 545 Artificial Neural Networks.

    Prerequisite by Topic

    Undergraduate level calculus, probability theory. Prior experience in a high-level programming language.


  • Introduction to Pattern Recognition
  • Probability Review
  • Statistics Review
  • Multivariate Analysis
  • Bayes Decision Theory
  • Parametric Techniques
  • Nonparametric Techniques
  • Unsupervised Learning and Clustering

    Computer Usage

    Almost all homeworks require computer simulations.


  • 1 Project 0.30
  • 1 Final 0.30
  • Homeworks 0.40