AL-603 (C) RGPV B.Tech
Pattern Recognition (PR)
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Syllabus, notes and important questions for AL-603 (C)
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Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Introduction and Mathematical Preliminaries 9 topics
- Principles of pattern recognition
- Uses of pattern recognition
- Mathematics for pattern recognition
- Classification and Bayesian rules
- Clustering vs classification
- Basics of linear algebra and vector spaces
- Eigenvalues and eigenvectors
- Rank of matrix
- Singular Value Decomposition (SVD)
Unit 2 Pattern Recognition Basics 15 topics
- Bayesian decision theory
- Classifiers
- Discriminant functions
- Decision surfaces
- Parameter estimation methods
- Hidden Markov models
- Dimension reduction methods
- Fisher discriminant analysis
- Principal component analysis (PCA)
- Non‑parametric techniques for density estimation
- Non‑metric methods for pattern classification
- Unsupervised learning
- Clustering algorithms: K‑means
- Clustering algorithms: Hierarchical clustering
- Other clustering methods
Unit 3 Feature Selection and Extraction 8 topics
- Problem statement and uses of feature selection
- Branch‑and‑bound algorithm for feature selection
- Sequential forward selection
- Sequential backward selection
- Cauchy‑Schwarz inequality in feature selection
- Probabilistic separability based feature selection criteria
- Inter‑class distance based feature selection criteria
- Principles of feature extraction
Unit 4 Visual Recognition 11 topics
- Human visual recognition system
- Low‑level modelling (e.g., feature extraction)
- Mid‑level abstraction (e.g., segmentation)
- High‑level reasoning (e.g., scene understanding)
- Detection and segmentation methods
- Context and scene analysis
- Importance and saliency detection
- Large‑scale search and recognition
- Egocentric vision systems
- Human‑in‑the‑loop interactive systems
- 3‑D scene understanding
Unit 5 Recent Advancements in Pattern Recognition 10 topics
- Comparison of classifier performance
- Basics of statistics for pattern recognition
- Covariance and its properties
- Data condensation techniques
- Feature clustering
- Data visualization
- Probability density estimation
- Visualization and aggregation methods
- Fuzzy C‑Means (FCM) and soft‑computing techniques
- Examples of real‑life datasets