CS-503 (B) Pattern Recognition Notes
5 notes for Pattern Recognition, unit by unit.
Notes for CS-503 (B)
- Unit 1 · Short notes Introduction Definitions · Data sets for Pattern · Application Areas and Examples of pattern recognition · Design principles of pattern recognition system · Classification and clustering · Supervised Learning
- Unit 2 · Short notes Classification Introduction · Application of classification · Types of classification · Decision tree · Naïve Bayes · Logistic regression
- Unit 3 · Short notes Unsupervised Learning & Clustering 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
- Unit 4 · Short notes Feature Extraction and Selection Introduction of feature extraction and feature selection · Types of feature extraction · Problem statement and Uses · Branch and bound algorithm · Sequential forward / backward selection algorithms · (l,r) algorithm
- Unit 5 · Short notes Recent Advances Recent advances in Pattern Recognition · Structural PR · SVMs · FCM · Soft computing and Neuro‑fuzzy techniques · Real‑life examples
More for CS-503 (B)
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- CS-503 (C) Cyber Security
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- CS-504 (B) Object Oriented Programming
- CS-504 (C) Introduction to Database Management Systems
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