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Subject Syllabus

AL603 (C) - Pattern Recognition

AIML · Semester 6

Course Modules

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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

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

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

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

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