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Subject Syllabus

CS-503 (B) - Pattern Recognition

CSE · Semester 5

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

Definitions Data sets for pattern recognition Application areas and examples of pattern recognition Design principles of a pattern recognition system Classification and clustering Supervised learning Unsupervised learning and adaptation Pattern recognition approaches Decision boundaries and decision regions Metric spaces and distances

Introduction to classification Application of classification Types of classification Decision tree Naive 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 and test set Standardization and normalization

Different paradigms of pattern recognition Representations of patterns and classes Unsupervised learning and clustering Criterion functions for clustering Iterative square-error partitional clustering: k-means Hierarchical clustering Cluster validation

Introduction to feature extraction and feature selection Types of feature extraction Problem statement and uses Branch and bound algorithm Sequential forward and backward selection algorithms (l,r) algorithm

Recent advances in pattern recognition Structural pattern recognition SVMs FCM Soft computing and neuro-fuzzy techniques Real-life examples Histogram rules Density estimation Nearest neighbour rule Fuzzy classification