The broad objective of this course is to make individuals who are doing their research and teaching on Pattern Recognition. Pattern Recognition techniques are widely used for many applications such as biometrics, medical imaging etc since a long time. The objective of this course is to give basic of pattern recognition concept with applications to computer vision, which help to upgrade the expertise and capabilities of the faculty members of various engineering institutions in India so that they can generate climate for research and enthusiasm for academic excellence. The objective of the course work is also to provide the theoretical fundamentals coupled with practical knowledge of pattern recognition. The hands-on training session will be conducted using MATLAB and Weka.


  • Introduction to Pattern Recognition.
  • Unsupervised Clustering Algorithms
  • Multi-Objective Clustering Algorithms
  • Statistical Pattern Recognition. Bayes Rule. Maximum Likelihood, Classification.
  • Maximum Likelihood Parameter Estimation
  • Principal Component Analysis, Linear Discriminant Functions
  • Support Vector Machines
  • Neural Networks, Multilayer Perceptron and Back-propagation
  • Hyperbox Classifier
  • Fuzzy Min-Max Neural Network
  • Decision Trees and Hierarchical Classification
  • Ensemble Classifiers; Bagging, Boosting
  • Deep Learning