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CS-503 (B) · Pattern Recognition/Official Syllabus

Pattern Recognition (CS-503 (B)) - Official Syllabus

1
Unit 1

Introduction

  • Definitions
  • data sets for Pattern
  • Application Areas and Examples of pattern recognition
  • Design principles of pattern recognition system
  • Classification and clustering
  • supervised Learning
  • unsupervised learning and adaptation
  • Pattern recognition approaches
  • Decision Boundaries
  • Decision region
  • Metric spaces
  • distances
2
Unit 2

Classification

  • introduction
  • application of classification
  • types of classification
  • decision tree
  • naïve bayes
  • logistic regression
  • support vector machine
  • random forest
  • K Nearest Neighbour Classifier and variants
  • Efficient algorithms for nearest neighbour classification
  • Different Approaches to Prototype Selection
  • Combination of Classifiers
  • Training set
  • test set
  • standardization and normalization
3
Unit 3

Different Paradigms of Pattern Recognition

  • Different Paradigms of Pattern Recognition
  • Representations of Patterns and Classes
  • Unsupervised Learning & Clustering: Criterion functions for clustering
  • Clustering Techniques: Iterative square -error partitional clustering – K means
  • hierarchical clustering
  • Cluster validation
4
Unit 4

Feature Extraction and Feature Selection

  • introduction of feature extraction and feature selection
  • types of feature extraction
  • Problem statement and Uses
  • Algorithms - Branch and bound algorithm
  • sequential forward / backward selection algorithms
  • (l,r) algorithm
5
Unit 5

Recent advances in Pattern Recognition

  • Recent advances in Pattern Recognition
  • Structural PR
  • SVMs
  • FCM
  • Soft computing and Neuro-fuzzy techniques
  • real-life examples
  • Histograms rules
  • Density Estimation
  • Nearest Neighbor Rule
  • Fuzzy classification

Reference Books

  • Pattern Classification — Richard O. Duda, Peter E. Hart, David G. Stork — John Wiley — 2nd Edition
  • Pattern Recognition and Machine Learning — C. M. Bishop — Springer
  • Pattern Recognition — S. Theodoridis, K. Koutroumbas — academic Press — 4th Edition
  • pattern Recognition: statistical, structural and neural approaches — Robert Schalkoff — JohnWiley & sons , Inc
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