Implemented Machine Learning Algorithms in Java

HW1 - Decision Tree, Regression Tree, Linear Regression with Normal Equations

HW2 - Linear Regression using Gradient Descent, Gradient Descent Logistic Regression, Perceptron Algorithm, Autoencoder Neural Network

HW3 - Gaussian Discriminant Analysis, Naive Bayes classifier (w/ Gaussian random variables, Bernoulli (Boolean) random variables, and Histograms)

HW4 - AdaBoost (w/ "Optimal" Decision Stumps), Adaboost on UCI datasets, Active Learning, Error Correcting Output Codes (w/ AdaBoost)

HW5 - PCA, Regularized Regression for feature selection, Image HAAR Feature Extraction on Digit Dataset w/ ECOC and AdaBoosting

HW6 - SVM with SMO solver

HW7 - k-Nearest Neighbors, Kernel density estimation w/ Gaussian kernel and polynomial kernel, Dual Perceptron with dot product and RBF kernels