MSc
Artificial Intelligence
Degree Programme

Optional Preparatory Semester

• LANG 7100 Advanced English for Postgraduate Studies

  This course is designed to enhance students’ academic language proficiency and communication skills. It prepares postgraduate students to engage confidently with academic texts and lectures with a technology and business focus while introducing them to key research practices and content relevant to their field of study. Students will develop a strong grasp of research methodologies, evidence-based writing, and critical analysis which are essential skills for success at the postgraduate study level. Through structured writing and speaking tasks aligned with higher education standards, and drawing on principles from established English for Specific Purposes (ESP) programs, the course uses discipline-specific materials to ensure practical, relevant, and meaningful learning.

• LIBA 7100 Academic Skills for Postgraduate Studies

  In this course, students will be introduced to a range of postgraduate study skills to facilitate successful completion of their postgraduate programme and allow them to begin the process of becoming collaborative and independent learners. The course focuses on developing critical thinking, AI literacy, research and communication. These skills will be introduced through investigations business and technology relating to the integration of AI. Beyond this, the course will create awareness of AI ethics and sustainability, using group work and report writing. Students will be introduced to, and use, a range of digital platforms as aids to effective communication through professional academic oral presentations and written reports using secondary sources. In that, the course allows students to adjust to expectations and conventions of postgraduate education in an English-speaking academic environment. In addition, students will be presented with reflective writing tools through a personal development portfolio that can aid in self-improvement, particularly in an academic setting

Offered in Fall Semester

• COMP 7101 Machine and Deep Learning Systems

  Machine and Deep Learning offers an in‑depth and practice‑focused exploration of state‑of‑the‑art neural network methods and their deployment in real‑world scenarios. The course develops advanced expertise in modelling complex, high‑dimensional data using modern architectures such as convolutional networks, recurrent networks, transformers, and generative models. Students learn to design, train, evaluate, and deploy deep learning systems using industry‑standard frameworks (e.g., PyTorch or TensorFlow). Strong emphasis is placed on rigorous experimental methodology, optimisation strategies, interpretability, and responsible use of large‑scale models.

• COMP 7102 Natural Language Processing

  Natural Language Processing (NLP) is a core discipline within Artificial Intelligence that focuses on the computational analysis, modelling and generation of human language. This module provides a comprehensive, postgraduate-level introduction to NLP by integrating foundational linguistic concepts with modern machine learning approaches used in contemporary language technologies. Students examine the full language-processing pipeline, including text preprocessing, vector-space representations, statistical modelling, neural sequence architectures, attention mechanisms and transformer-based models.

• COMP 7103 AI Ethics and Governance

  This course provides a critical and interdisciplinary introduction to AI ethics and governance. Students critically analyse AI systems, outputs, and impacts using interdisciplinary methods from ethics, law, sociology, critical theory, and will explore industry standards such as ACM or IEEE ethical frameworks. They will examine developing legislation including the EU AI Act, UK regulatory approaches, GDPR, and major international governance standards. The course develops students' ability to assess risks, analyse AI outputs for accuracy and bias, and consider the broader societal and cultural impacts of AI systems.

Offered in Spring Semester

• COMP 7104 Reinforcement Learning

  This module introduces reinforcement learning as a key paradigm within contemporary artificial intelligence. Students develop an understanding of how learning agents operate within interactive environments and how reinforcement learning techniques are applied to complex decision-making problems. The course blends theory and practice, enabling students to apply reinforcement learning approaches to realistic scenarios and to evaluate their effectiveness. Emphasis is placed on critical analysis of strengths and limitations, methodological rigour, and real-world considerations, supporting students in making informed judgements about the appropriate use of reinforcement learning in applied AI contexts.

• COMP 7100 Research Methods for Computing

  This course introduces students to the principles, methodologies and analytical techniques used in computing research. It provides an understanding of how knowledge is generated, evaluated and communicated within the discipline, with emphasis on research design, data collection, data analysis and academic writing. Students learn to critically interpret research literature, formulate research questions, select appropriate methodological approaches, and reflect on ethical and professional considerations relevant to computing research. The course prepares students for projectbased work and professional inquiry by developing core skills required to conduct and evaluate research in academic and applied contexts.

• COMP 7106 Advanced Computer Vision

  This course provides an in‑depth exploration of advanced computer vision theory and practice, focusing on modern deep learning-based methods for image and video processing. Students develop a thorough understanding of key architectures, including convolutional neural networks, transformers for vision, and diffusion‑based generative models. The course also examines topics such as object detection, semantic segmentation, representation learning, and multimodal vision systems. Emphasis is placed on developing the ability to critically evaluate and implement state‑of‑the‑art models using contemporary frameworks, and to apply vision systems to complex real world problems.

• COMP 7105 AI Security

  AI Security is focused on the protection, robustness, and trustworthy operation of artificial intelligence systems. The module examines the vulnerabilities inherent in modern machine learning architectures, the techniques used to attack them, and the defences required to secure AI in real-world applications. Students study adversarial machine learning, model extraction, data poisoning, backdoor attacks, prompt-based LLM exploitation, privacy attacks, to name a few. The module integrates both theoretical perspectives and practical methodologies enabling students to evaluate and implement security controls across the AI lifecycle.

Offered in Fall, Spring and Summer Semesters

Plus either both of:

• COMP 7500 Professional Research Project

  This course provides students with the opportunity to undertake a substantial practical or research‑based project that demonstrates advanced application of various computing domains. Students may choose either a practical project, focused on the design and implementation of a system or solution, or a research project, focused on empirical, experimental or theoretical investigation. Working independently under supervision, students define a problem or research question, review relevant literature, select appropriate methods, and deliver a significant piece of work that evidences Level 7 technical, analytical and professional competence.

• COMP 7901 Internship Placement

  This course provides students with the opportunity to undertake a supervised internship within a computing, software, or technology-focused organisation. Students apply applied computing knowledge, software engineering skills and professional competencies to real‑world industry problems and workflows.

Or:

• COMP 7501 Extended Professional Research Project

  This course provides students with an extended supervised project in a computing, software engineering or technology-focused field. The extended professional research project enables students to participate in longer-term projects, systems development, operational processes, or research requiring advanced applied computing skills.