Learning from Data
Increasing amounts of data are being captured, stored and made available electronically. The aim of the Learning from Data specialist area is to train students in techniques to analyze, interpret and exploit such data, and to understand when particular methods are suitable and/or applicable. These techniques derive from disciplines such as machine learning, probabilistic and statistical modelling, pattern recognition and neural networks, and are sometimes collectively referred to as data mining. The specialist area will prepare students for entry into PhD programmes or for employment in commercial environments and/or scientific/engineering research.
Students registered in this Specialist Area are recommended to select at least fifty credit points from these courses, including both the core courses. Please note courses are subject to availability.
See also guidance from one of the machine learning lecturers. The guidance includes external statistics courses that could be of interest, and general course selection advice. In particular, Introductory Applied Machine Learning (IAML) is strongly recommended unless you have taken a similar course before. Both core courses have extensive mathematical pre-requisites. These requirements are outlined, with preparatory material, on their webpages. If you do not have the required mathematical background you can still do a significant number of courses involving learning from data while following another specialism.