This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood modelling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.

On satisfying the requirements of this course, students will have the knowledge and skills to:

• understand a number of models for supervised, unsupervised, and reinforcement machine learning
• describe the strength and weakness of each of these models
• define the mathematical objects from Linear Algebra, Statistics, and Probability Theory used in these machine learning models
• implement these machine learning models on a computer
• design test procedures in order to evaluate a model
• combine several models in order to gain better results
• make choices for a model for new machine learning tasks based on reasoned argument

Enrolment in the Master of Computing