(Pattern Recognition)
- Introduction- applications and introducing components of a general pattern recognition systems
- Fundamental theory in linear algebra probability and statistics
- Statistical decision functions, performance evaluation
- Parameter estimation
ML and BM approach
- Density estimation
Histogram, KNn, Parzen,
KNn Classifier
- Dimensionality Reduction,
PCA, FA, CCA
- Linear Decision functions,
Iterative techniques,
Hk,
SVM
- Clustering,
KNN,
Hierarchical
Spectral clustering