Course Details

Subject {L-T-P / C} : EE6152 : Pattern Recognition {3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Dipti Patra


Pattern Recognition: Feature Extraction and classification stages, Different approaches to pattern recognition. Statistical Pattern Recognition : Hypothesis testing, Linear classifiers, Parametric and nonparametric classification techniques, Unsupervised learning and clustering, Syntactic pattern recognition, Fuzzy set Theoretic approach to PR, Applications of PR : Speech and speaker recognition, Character recognition, Scene analysis.

Course Objectives

  1. Provide knowledge of models, methods and tools used to solve regression,
    classification, feature selection and density estimation problems
  2. Provide knowledge of current research topics and issues in Pattern Recognition and
    Machine Learning
  3. Provide hands-on experience in analyzing and developing solutions/algorithms
    capable of learning

Course Outcomes

• Explain and compare a variety of pattern classification, structural pattern recognition techniques.
• Apply performance evaluation methods for pattern recognition, and critique comparisons of techniques made in the research literature.
• Apply pattern recognition techniques to real-world problems.
• Implement simple pattern classifiers, classifier combinations, and structural pattern recognizers.

Essential Reading

  1. Peter E. Hart, Richard O. Duda, David G. Stork, Pattern Classification, Wiley
  2. Christopher Bishop, Pattern Recognition & Machine Learning, Springer

Supplementary Reading

  1. T.Y. Young & King-Sun Fu, Handbook of Pattern Recognition & Image Processing, Academic Press
  2. Peebles, Peyton Z, Probability, Random Variables & Random Signal Principles, McGraw-Hill

Journal and Conferences

  1. IEEE Transaction on Pattern Analysis and Machine Intelligence, IEEE conference on Computer Vision & Pattern Recognition
  2. Elsevier Journal on Pattern Recognition