COMP30120 Introduction to Machine Learning

Academic Year 2016/2017

The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as instructing students in the practical aspects of applying machine learning techniques to real datasets. Key techniques in supervised machine learning will be covered. These include k-Nearest Neighbour classifiers, Decision Trees, and Naive Bayes classification. In unsupervised machine learning, a number of popular clustering algorithms will be presented in detail (e.g. k-Means Clustering, Hierarchical Clustering). Additional topics covered include ensemble learning, dimension reduction, and network analysis. This module has a practical focus and students will be expected to complete three coursework assignments, each involving the analysis of data using machine learning methods and the interpretation of the outputs of those methods. COMP30120 requires significant mathematical ability, as some of the algorithms require an understanding of linear algebra and statistical concepts.