PROGRAMME / 01
Master · Artificial Intelligence and Data Science
Combine statistics, machine learning and data engineering to develop predictive models and deploy intelligent applications.
For applicants with relevant prior study. Develop advanced technical and research skills. Planned workload: 120 credits.
YEAR 01Advanced Learning and Inference
- Advanced Machine Learning
- Bayesian Modelling
- Causal Inference
- Data Architecture
- Large Language Models and Multimodal Learning
- Research Methods and Reproducibility
YEAR 02Trustworthy Models and Research Practice
- Model Optimisation and Deployment
- Uncertainty Assessment
- Trustworthy AI
- Privacy-Preserving Machine Learning
- Scientific Data Research Topics
- Master’s Dissertation and Defence
SHARED LEARNING
Shared foundations. Specialist depth.
Selected AI, data processing, data engineering and deployment modules may be shared with related PTI programmes.
Specialist focus: Statistical inference, scientific data analysis and reliable prediction.
Assessment and outcomes
Assessment covers data quality, experimental design, generalisation and interpretation. Predictive projects must document data splits, prevent leakage and compare results with appropriate baselines.
Build a reproducible predictive system using public or authorised data. Submit code, data documentation, model evaluation and a technical report.