Machine Learning Lab

Experiments (Python): FIND-S algorithm for the most specific hypothesis from training data (.CSV); Candidate-Elimination algorithm for the set of all consistent hypotheses; Decision tree based ID3 algorithm with classification of a new sample; Artificial Neural Network with Backpropagation algorithm; Naive Bayesian classifier on a .CSV training set with accuracy computation; Document classification using Naive Bayes with accuracy, precision and recall; Bayesian network on medical data — heart disease diagnosis (Heart Disease Data Set); EM algorithm vs k-Means clustering comparison on a .CSV dataset; k-Nearest Neighbour classification of the Iris dataset with correct/wrong predictions; Non-parametric Locally Weighted Regression with graphs.