MSc Artificial Intelligence

Module 1: Introduction to Artificial Intelligence (30 credits)

❚ Foundations of AI: classical approaches, narrow vs general AI

❚ Probabilistic reasoning: Bayesian networks, Markov decision processes

❚ Neural networks: perceptrons, multi-layer architectures, activation functions

❚ Natural Language Processing: practical techniques and language models

❚ Agent-based systems: autonomous agents and multi-agent environments

 

Module 2: Advanced Artificial Intelligence (30 credits)

❚ Machine learning: supervised and unsupervised techniques, ensemble methods

❚ Deep learning: CNNs, RNNs, LSTMs, transformers and attention mechanisms

❚ Generative AI: GANs, large language models (LLMs), synthetic data

❚ Computer vision: object detection, segmentation, video classification

❚ Reinforcement learning and transfer learning

 

Module 3: Data Concepts, Ethics, Law and Governance for AI (30 credits)

❚ Data concepts: big data, data preparation, cleansing, integrity

❚ Legal frameworks: GDPR, EU AI Act, US regulations, international conflicts

❚ AI ethics: bias and fairness, accountability, ethical data strategies

❚ Sustainability and environmental implications of AI systems

❚ Equality, diversity and inclusion in AI design

 

Module 4: Data Driven Decision Making (30 credits)

❚ Human perception: psychology, cognitive biases, colour theory

❚ Data visualisation: design principles, multivariate and interactive methods

❚ Temporal and spatial analysis: time-series, heat maps, geospatial methods

❚ Data storytelling: narrative building and audience communication

❚ Emerging technologies: AR/VR for data exploration, ML-enhanced visualisation

 

Module 5: Masters Project (60 credits)

❚ Research methods and literature review

❚ Project proposal development and planning

❚ Legal, ethical, and professional considerations

❚ Independent project implementation

❚ Project report (8,000 words) with 10-minute confirmatory interview