Machine-Learning-Pattern-Recognition

Machine Learning / Pattern Recognition algorithms implementation

Algorithms implemented:

1. Singular Value Decomposition, Eigen Value Decomposition on an image dataset
2. Polynomial Regression
3. Bayesian Classifier on real & synthetic data
4. Fisher discriminant based classifier on features of images
5. Gaussian Mixture Model on Image dataset & Synthetic dataset
6. Hidden Markov Model on MFCC TIDIGITS online handwritten dataset
7. Dynamic Time Warping on MFCC TIDIGITS online handwritten dataset
8. Principal Component Analysis on images of faces
9. Bayes' classifier on Parzen window method using Gaussian kernel on an Image dataset
10. Fisher discriminant based classifier on an Image dataset
11. Perceptron based classifier on an Image dataset
12. Support vector machine-based classifier (using linear kernel) on Speech and OCR data
13. Multi-layer feed-forward neural network-based classifier on Speech and OCR data

Plots:

1. Image Reconstruction
2. Decision Boundaries
3. Confusion Matrix
4. Receiver Operating Characteristic (ROC) curve
5. Detection Error Tradeoff (DET) curve