Implementation of various machine learning algorithms.
Implements basic gradient descent.
Implements the recursive id3 algorithm and chooses best split based on maximum entropy decrease.
Implements bagging and boosting to learn an ensemble of decision trees.
Calculates Principle Component Analysis and decomposes dataset into eigenvalues and eigenvectors. Plots cumulative variance chart.
Implements spectral clustering of generated dataset using PCA and plots the kmeans labels.