Statistics and Artificial Intelligence MSc

There are three phases to the learning journey. In the first phase, core knowledge and skills in statistical methods and inference are strengthened. Topics covered include frequentist and Bayesian inference, data analysis, generalised linear models, statistical computing, and communication. These areas provide the foundation for building an understanding of the mechanics of AI and machine learning.

In the second phase, more advanced study is undertaken through modules in supervised and unsupervised learning, deep learning, and statistical models for complex processes. Alongside the statistical and computational principles used to infer structure in these models, practical insight is developed through their application to real data.

In the third phase, an independent research project on a chosen topic is completed under the supervision of experts from the School of Mathematical Sciences. This may involve advancing understanding of a topic introduced in a taught module, investigating a subject not previously covered, or conducting an extended data analysis.