MTech Pattern Recognition syllabus for 2 Sem 2020 scheme 20SCE242

Module-1 Introduction 0 hours

Introduction:

Definition of PR, Applications, Datasets for PR, Different paradigms for PR, Introduction to probability, events, random variables, Joint distributions and densities, moments. Estimation minimum risk estimators, problems

Module-2 Representation 0 hours

Representation:

Data structures for PR, Representation of clusters, proximity measures, size of patterns, Abstraction of Data set, Feature extraction, Feature selection, Evaluation

A d v e r t i s e m e n t
Module-3 Nearest Neighbour based classifiers & Bayes classifier 0 hours

Nearest Neighbour based classifiers & Bayes classifier:

Nearest neighbour algorithm, variants of NN algorithms, use of NN for transaction databases, efficient algorithms, Data reduction, prototype selection, Bayes theorem, minimum error rate classifier, estimation of probabilities, estimation of probabilities, comparison with NNC, Naive Bayes classifier, Bayesian belief network

Module-4 Naive Bayes classifier 0 hours

Naive Bayes classifier,

Bayesian belief network, Decision Trees: Introduction, DT for PR, Construction of DT, splitting at the nodes, Over fitting & Pruning, Examples, Hidden Markov models: Markov models for classification, Hidden Markov models and classification using HMM

Module-5 Clustering 0 hours

Clustering:

Hierarchical (Agglomerative, single/complete/average linkage, wards, Partitional (Forgy’s, kmeans, Isodata), clustering large data sets, examples, An application: Handwritten Digit recognition

 

Course outcomes:

At the end of the course the student will be able to:

  • Explain pattern recognition principals
  • Develop algorithms for Pattern Recognition.
  • Develop and analyze decision tress.
  • Design the nearest neighbor classifier.
  • Apply Decision tree and clustering techniques to various applications

 

Question paper pattern:

The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 60.

  • The question paper will have ten full questions carrying equal marks.
  • Each full question is for 20 marks.
  • There will be two full questions (with a maximum of four sub questions) from each module.
  • Each full question will have sub question covering all the topics under a module.
  • The students will have to answer five full questions, selecting one full question from each module.

 

Textbook/ Textbooks

1 Pattern Recognition (An Introduction) V Susheela Devi, M Narsimha Murthy Universities Press 2011

2 Pattern Recognition & Image Analysis Earl Gose, Richard Johnsonbaugh, Steve Jost PH 1996.

 

Reference Books

1 Pattern Classification Duda R. O., P.E. Hart, D.G. Stork John Wiley and sons 2000.

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