Machine Learning

AY2016/2017 Semester 2

Overview of machine learning and its applications; Decision Theory and Bayes Models; Classifier Evaluation; Classification: Decision trees, artificial neural networks, linear and kernelized support vector machines, K-nearest neighbour classifiers, linear regression and its kernelized extension; Ensemble Learning; Clustering; Dimension Reduction; Density Estimation; Graphical Models; Applications