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AL-603 (C) · Pattern Recognition/Official Syllabus

Pattern Recognition (AL-603 (C)) - Official Syllabus

1
Unit 1

Introduction and mathematical Preliminaries

  • Principles of pattern recognition: Uses
  • mathematics
  • Classification and Bayesian rules
  • Clustering vs classification
  • Basics of linear algebra and vector spaces
  • Eigen values and eigen vectors
  • Rank of matrix and SVD
2
Unit 2

Pattern Recognition basics

  • Bayesian decision theory
  • Classifiers, Discriminant functions, Decision surfaces
  • Parameter estimation methods
  • Hidden Markov models
  • dimension reduction methods
  • Fisher discriminant analysis
  • Principal component analysis
  • non-parametric techniques for density estimation
  • nonmetric methods for pattern classification
  • unsupervised learning
  • algorithms for clustering: Kmeans, Hierarchical and other methods
3
Unit 3

Feature Selection and extraction

  • Problem statement and uses
  • Branch and bound algorithm
  • Sequential forward and backward selection
  • Cauchy Schwartz inequality
  • Feature selection criteria function: Probabilistic separability based
  • Interclass distance based
  • Feature Extraction: principles
4
Unit 4

Visual Recognition

  • Human visual recognition system
  • Recognition methods: Low-level modelling (e.g. features)
  • Midlevel abstraction (e.g. segmentation)
  • High-level reasoning (e.g. scene understanding)
  • Detection/Segmentation methods
  • Context and scenes, Importance and saliency
  • Large-scale search and recognition
  • Egocentric vision, systems
  • Human-in-the-loop interactive systems
  • 3D scene understanding
5
Unit 5

Recent advancements in Pattern Recognition

  • Comparison between performance of classifiers
  • Basics of statistics, covariance and their properties
  • Data condensation, feature clustering
  • Data visualization, Probability density estimation, Visualization and Aggregation
  • FCM and soft-computing techniques
  • Examples of real-life datasets

Laboratory Experiments

  • Data extraction
  • Pre-processing of images
  • Image segmentation
  • Image classification

Reference Books

  • Pattern Recognition and Machine Learning — Christopher M. Bishop — Springer
  • Pattern Classification — Richard O. Duda, Peter E. Hart, David G. Stork — Wiley
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