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AL-603 (B) · Data and Visual Analytics/Official Syllabus

Data and Visual Analytics (AL-603 (B)) - Official Syllabus (AL-603)

1
Unit 1

MODULE-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

MODULE- 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
  • non metric methods for pattern classification
  • unsupervised learning
  • algorithms for clustering: K means Hierarchical and other methods
3
Unit 3

MODULE-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 and Inter class distance based
  • Feature Extraction: principles.
4
Unit 4

MODULE-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

MODULE-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
  • LIST OF EXPERIMENTS :
6
Unit 6

== END OF UNITS==

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