MSc – Artificial Intelligence & Machine Learning

Module Introduction

This module provides a rigorous foundation in artificial intelligence (AI) and machine learning (ML) at Level 7. It develops critical understanding of core concepts, algorithms, data representations, and evaluation methods used in modern AI systems. Students will analyse problem formulations, select appropriate models, and assess performance using evidence-based methods. The module is aligned with UK FHEQ Level 7 expectations and RSBT academic regulations, emphasising analytical rigour, ethical awareness, and professional application.

Module Aims

• Develop advanced understanding of AI and ML concepts, paradigms, and terminology.
• Enable learners to formulate problems and select appropriate ML approaches.
• Build competence in data preparation, model training, and evaluation.
• Enhance critical awareness of limitations, bias, and ethical considerations in AI systems.
• Prepare learners for advanced applied and research-oriented modules in the programme.

Learning Outcomes

• Critically evaluate key AI and ML concepts, algorithms, and assumptions.
• Formulate real-world problems as supervised or unsupervised learning tasks.
• Apply appropriate methods for data preprocessing, model selection, and evaluation.
• Interpret results using quantitative metrics and critical reasoning.
• Communicate technical analysis and recommendations using appropriate academic and professional conventions.

Indicative Content

• AI vs ML; problem types and learning paradigms
• Data representations, features, and preprocessing
• Supervised learning: regression, classification, k-NN, decision trees, ensembles
• Unsupervised learning: clustering, dimensionality reduction
• Model training, validation, and evaluation metrics
• Overfitting, bias–variance trade-off, and regularisation
• Introductory optimisation and gradient-based methods
• Responsible AI: bias, fairness, transparency, and ethics
• Tools and workflows for ML experimentation
• Reproducibility and reporting results

Learning and Teaching Strategy

Delivery will include interactive lectures, practical labs, case studies, and guided reading. Students will undertake substantial independent study, including experimentation with datasets and critical review of academic sources. As a 30-credit module, the workload and depth of analysis are proportionately higher than 15-credit modules.

Assessment Strategy

CW1: Technical portfolio or problem set demonstrating data preparation, modelling, and evaluation. CW2 (60%): Individual Report (3,500–4,000 words) analysing an ML problem, justifying model choices, evaluating results, and reflecting on ethical and practical considerations, with Harvard referencing.

Recommended Reading

• Murphy, K. (2022). Machine Learning: A Probabilistic Perspective. MIT Press.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• James, G. et al. (2021). An Introduction to Statistical Learning. Springer.
• Mitchell, T. (1997). Machine Learning. McGraw-Hill.

Module Introduction

This module provides advanced study of deep learning and its applications to natural language processing (NLP) and computer vision (CV). Students will critically analyse modern neural architectures, representation learning, and training strategies, and apply them to text and image/video tasks. The module emphasises experimental rigour, evaluation, ethical considerations, and deployment constraints. It is aligned with UK FHEQ Level 7 expectations and RSBT academic regulations, requiring critical judgement, technical depth, and professional application.

Module Aims

• Develop advanced understanding of deep learning architectures and training methods.
• Enable learners to apply neural models to NLP and computer vision problems.
• Build competence in model evaluation, optimisation, and error analysis.
• Enhance awareness of data bias, robustness, interpretability, and ethical issues in DL systems.
• Prepare learners to design and critique end-to-end DL solutions in applied settings.

Learning Outcomes

• Critically evaluate deep learning models and their assumptions for NLP and CV tasks.
• Design and implement neural architectures for text and image-based applications.
• Apply appropriate training, regularisation, and optimisation strategies.
• Evaluate models using quantitative metrics and qualitative error analysis.
• Communicate technical designs, results, and limitations using appropriate academic and professional conventions.

Indicative Content

• Neural networks review; representation learning
• Convolutional neural networks (CNNs) for vision
• Recurrent networks, Transformers, and attention mechanisms
• Pre-trained models and transfer learning
• Natural language processing: text classification, sequence labelling, embeddings, LLM foundations
• Computer vision: image classification, detection, segmentation
• Training at scale, regularisation, and optimisation techniques
• Evaluation, benchmarking, and error analysis
• Robustness, fairness, interpretability, and safety
• Deployment considerations and resource constraints

Learning and Teaching Strategy

Delivery will include lectures, hands-on labs, paper discussions, and guided reading. Students will undertake substantial independent study, including implementing and evaluating DL models on real datasets. As a 30-credit module, the workload and depth of analysis are proportionately higher than 15-credit modules, with emphasis on experimentation, critical evaluation, and reflective reporting.

Assessment Strategy

CW1: Practical portfolio implementing and evaluating DL models for NLP and/or CV tasks. CW2 (60%): Individual Report (3,500–4,000 words) analysing model design choices, training strategy, evaluation results, and ethical/operational considerations, with Harvard referencing.

Recommended Reading

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• Jurafsky, D. & Martin, J. (2023). Speech and Language Processing (draft).
• Szeliski, R. (2022). Computer Vision: Algorithms and Applications. Springer.
• Vaswani, A. et al. (2017). Attention Is All You Need.

Module Introduction

This module focuses on the strategic, ethical, and operational aspects of deploying artificial intelligence and machine learning systems in organisations. Students will critically evaluate how AI creates value, how it should be governed, and how systems can be deployed, monitored, and scaled responsibly. The module integrates strategy, risk, compliance, and change management with technical deployment considerations. It is aligned with UK FHEQ Level 7 expectations and RSBT academic regulations, requiring critical judgement, professional awareness, and applied decision-making.

Module Aims

• Develop strategic understanding of how AI and ML create organisational value.
• Enable learners to design governance, risk, and compliance approaches for AI systems.
• Build competence in deployment patterns, monitoring, and lifecycle management of ML systems.
• Enhance critical awareness of ethics, bias, transparency, and regulatory considerations.
• Prepare learners to lead and manage AI initiatives within organisational contexts.

Learning Outcomes

• Critically evaluate AI strategies and value creation opportunities in different sectors.
• Design governance and risk management frameworks for AI systems.
• Analyse deployment architectures, monitoring, and lifecycle management approaches.
• Assess ethical, legal, and societal implications of AI applications.
• Communicate strategic and operational recommendations using appropriate academic and professional conventions.

Indicative Content

• AI strategy and digital transformation
• Business cases, ROI, and portfolio management for AI initiatives
• AI governance, policy, and organisational capability
• Ethics, bias, fairness, transparency, and accountability
• Regulation, standards, and compliance (e.g., data protection, AI governance)
• Deployment architectures: batch, real-time, edge
• Model monitoring, drift, and lifecycle management
• Security, privacy, and resilience in AI systems
• Change management and adoption
• Future trends and responsible innovation

Learning and Teaching Strategy

Delivery will include lectures, case studies, simulations, policy analysis, and guided reading. Students will undertake substantial independent study, including critical evaluation of organisational AI strategies and deployment plans. As a 30-credit module, the workload and depth of analysis are proportionately higher than 15-credit modules, with emphasis on strategic judgement and applied decision-making.

Assessment Strategy

CW1: Case-based analysis of an organisation’s AI strategy, governance, and deployment approach. CW2 (60%): Individual Report (3,500–4,000 words) developing an AI strategy and deployment roadmap for an organisation, including governance, risk, ethical considerations, and implementation plan, with Harvard referencing.

Recommended Reading

• Floridi, L. et al. (2018). AI4People—An Ethical Framework for a Good AI Society.
• European Commission. (2024). AI Act and Regulatory Guidance (latest).
• McKinsey Global Institute. (latest). The State of AI.
• Sculley, D. et al. (2015). Hidden Technical Debt in Machine Learning Systems.

Module Introduction

This module is the capstone of the MSc Artificial Intelligence & Machine Learning programme and represents a substantial, independent piece of work at Level 7. Students will undertake a research-focused dissertation or an applied industry project addressing a complex problem in AI/ML. The project requires critical engagement with literature, appropriate methodology, ethical compliance, and professional-quality reporting. As a 60- credit module, it demands a high level of autonomy, depth of analysis, and originality, aligned with UK FHEQ Level 7 expectations and RSBT academic regulations.

Module Aims

• Enable students to design and execute an independent research or applied project in AI/ML.
• Develop advanced skills in problem definition, methodology selection, and project management.
• Strengthen critical engagement with academic literature and professional practice.
• Ensure ethical, responsible, and reproducible research and development practices.
• Produce a substantial, coherent, and professionally presented dissertation or project report.

Assessment Strategy

Single major submission: Dissertation / Project Report (12,000–15,000 words, excluding references and appendices), demonstrating independent work, critical analysis, methodological rigour, and professional presentation. The submission may be accompanied by artefacts (e.g., code, datasets, models) where appropriate.