Course Title: 
Pattern Recognition
Course Code: 
Year Taught: 
Postgraduate (PG)
School of Engineering

'Pattern Recognition' is an Elective (Computer Vision Stream) course offered for the M. Tech. in Computer Science and Engineering program at School of Engineering, Amrita Vishwa Vidyapeetham.

Introduction to Pattern Recognition,Tree Classifiers -Decision Trees: CART, C4.5, ID3., Random Forests. Bayesian Decision Theory. Linear Discriminants. Discriminative Classifiers: the Decision Boundary- Separability, Perceptrons, Support Vector Machines. Parametric Techniques- Maximum Likelihood Estimation, Bayesian Parameter Estimation, Sufficient Statistics. Non -Parametric Techniques-Kernel Density Estimators, Parzen Window, Nearest Neighbor Methods. Feature Selection- Data Preprocessing, ROC Curves, Class Separability Measures,Feature Subset Selection,Bayesian Information Criterion. The Curse of Dimensionality-Principal Component Analysis. Fisher Linear Discriminant, Singular Value Decomposition, Independent Component Analysis, Kernel PCA Locally Linear Embedding.Clustering-. Sequential Algorithms, Hierarchical Algorithms,Functional Optimization-Based Clustering,Graph Clustering, Learning Clustering, Clustering High Dimensional Data, Subspace Clustering,Cluster Validity Measures, Expectation Maximization, Mean Shift. Classifier Ensembles-Bagging, Boosting / AdaBoost. Graphical Models- Bayesian Networks, Sequential Models- State-Space Models, Hidden Markov Models, Context Dependent Classification. Dynamic Bayesian Networks.


  1. Duda, R.O., Hart, P.E., and Stork, D.G. “Pattern Classification”. Second Edition, Wiley- Interscience. 2003.
  2. Theodoridis, S. and K. Koutroumbas, “Pattern Recognition”, Fourth Edition, San Diego, CA: Academic Press, 2009.
  3. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
  4. Earl Gose, Richard Johnsonbaugh and Steve Jost, “Pattern Recognition and Image Analysis”, Prentice Hall of India, 2002.