Pattern Recognition
- Some of the basics needed for studying the statistical pattern recognition methods
- Introduction of some of the Basics of decision theory, Information theory and Lagrange Multipliers
- Some parametric probability density functions
- Some non-parametric density functions
- Linear discriminant analysis
- Support vector machine basics and its application as a binary (and multi-Class) classifier. Employing the SVM in the Field of Regression
- K-means, ISODATA, Fuzzy K-means and GMM based Clustering methods
- Introduction of Mixture of Guassian Model (GMM). Data Modelling via GMM, Using ML Algorithm. The EM training algorithm Type 1 and 2
- Introduction of the Hidden Markov Model (HMM) and its Basics, its applications in sequential data Modelling and introducing the three principle problems of this model
- The Forward-backward (Baum Weltch) Algorithm in HMM
- Introduction of the Viterbi Algorithm and its Applications in HMM-based speech modeling
- Principal component analysis
- Application of PCA in dimentional reduction problems
- Introduction and Analysis of probabilistic PCA, Factor Analysis(FA) and Kernel PCA (KPCA)
- Nonlinear PCA and its Realization in the form of Artificial Neural Networks (under Auto-Encoder structures)