COURSE CURRICULUM

The core modules of the MSc in Statistics and Artificial Intelligence provide a comprehensive foundation in advanced statistical theory, computational methods and cutting-edge AI techniques. Advanced Statistical Modelling develops skills in constructing and evaluating complex statistical models for real-world data, while Estimation and Inference builds a deep understanding of how to draw reliable conclusions from uncertainty. Computing and Algorithms for Statistics introduces essential computational tools, numerical methods and algorithmic approaches needed to implement modern statistical and machine learning models effectively. Machine Learning and Deep Learning explore both classical and state-of-the-art approaches to pattern recognition, prediction and automated decision-making, equipping students to apply and adapt models across diverse applications. Hidden-Process Models expands analytical capabilities by focusing on time-dependent and latent-state problems, such as state-space and hidden Markov processes used in fields like finance, biomedical research and climate science. The Statistics Dissertation allows students to apply their knowledge through an independent research project, tackling a complex problem with methodological rigour and computational practicalities. Together, these modules ensure graduates develop strong analytical insight, programming and modelling expertise, and the ability to contribute to innovation in AI and statistical research. This skillset prepares students for advanced careers in data science, AI engineering, quantitative analysis, or further doctoral study.