MSc Computer Science with Artificial Intelligence

To apply, you’ll need to meet the following entry requirements: 

• A UK honours degree at 2:2 or above (or equivalent international qualification) in a subject other than computing. 

If you hold a non-honours degree and 2 years’ professional experience in one or more computing-related roles, you’re still encouraged to apply. We’ll review your academic and professional background on a case-by-case basis. Applicants without a first degree but with more than two years’ relevant professional experience in computing-related roles may be considered.

Thinking of transferring institutions or have you studied before? You can apply to transfer up to 60 credits towards your master's degree. Please note that credits can’t be awarded for the research module of this programme. 

These credits must be relevant, current, and aligned with the subject matter of your chosen MSc Computer Science pathway. 

• Review our recognition of prior learning process.

• Speak to our Enrolment Team.

• Submit a recognition of prior learning form alongside your application.

If English isn't your first language, you’ll be asked to provide proof of your English language proficiency in one of the following forms. Alternatively, you may be accepted if you have previously studied in English at an appropriate level and attended a recognised institution.

IELTS

• Evidence of a score of IELTS Level 6.0 or above with no element below 5.5. 

TOEFL iBT®

• Evidence of a score of 79 overall (with 18 in reading, 17 in listening, 20 in speaking and 21 in writing). 

Trinity College London Integrated Skills in English (ISE)

• Evidence of a score of ISE II with distinction in each skill.

Cambridge Certificate of Proficiency or Cambridge Certificate of Advanced English

• Evidence of a score of 170 overall, with 160 in each component. 

On this online Computer Science with Artificial Intelligence programme, you’ll study a series of carefully designed modules. With flexible monthly starts, you’ll join the next available module and study alongside a cohort of computer science master's students learning the same subject at the same time.

• Fundamentals of Artificial Intelligence (AI)
  Understand the core ideas behind AI and machine learning – and how they’re strategically applied to common business problems. You’ll experiment with tools to solve realistic problems, interpret results, and weigh up the commercial and ethical impact of using AI. 

• Machine Learning: Principles and Programming
  Dive into the algorithms that power predictive models. You’ll programme, train, and test machine learning systems using the industry standard Python programming language, applying them to real datasets to solve problems in areas like forecasting, classification, and pattern detection.

• Large Language Models and Prompt Engineering
  Step into one of computing’s most dynamic frontiers. Discover how large language models (LLMs) are built with reinforcement learning, master prompt engineering techniques, and explore the power of open-source LLMs across data science, automation, and intelligent deep learning and problem-solving. 

• Software Engineering
  Master the principles and practices that make complex software systems reliable, maintainable, and scalable. You’ll use established development approaches, learn how to handle complexity, and see how new trends are influencing the way software is built.

• Modern Database Systems
  Build high-performance databases that power intelligent applications. Learn to design relational databases and NoSQL systems, optimise queries for speed and efficiency, use industry standard Tools such as MariaDB and MongoDB, and make strategic storage decisions that support scalability and security.

• Information Systems Development
  Analyse organisational needs and develop user-centred, secure information systems, using HTML, CSS and JavaScript programming languages. Integrate computer science theory with practical design and development methods to build information systems that work seamlessly in challenging organisational contexts.

• Computer Networks
  Study protocols, architectures, and performance management, and learn how to design, configure, and optimise secure, reliable networks – and gain the skills to troubleshoot them under pressure. 

• Cloud Computing
  Deploy intelligent operating systems in the cloud with confidence. Explore service and deployment models, virtualisation, networking, and containerisation, and address security and performance considerations. 

• Project Management and the Computing Professional
  Lead AI and computing projects from idea to delivery. Master project planning, risk management, and communication strategies that keep technical teams aligned and ensure successful completion of a project. 

• Research Development
  Transform your ideas into a focused, actionable plan. Strengthen your research methods, refine your proposal, and evaluate literature with a critical eye – setting the stage for a final research project that’s both ambitious and achievable. 

• Research Project
  Conduct independent research in a topic that matters to you – for example, large language models, data analytics, or machine learning. You’ll demonstrate original thinking, critical evaluation, and the ability to deliver practical solutions to your research proposal. 

Across your MSc, you’ll complete a mix of assessments designed to stretch your thinking, strengthen your communication skills, and bring your learning to life. Each module (except your final research modules) includes three assignments – helping you build confidence, test ideas and apply theory in more than one way. 

Here’s a snapshot of the main types of assessments you’ll complete across your core modules. Want to know what assessments you’ll sit for each module, or just need more detail? Request more info and our team would love to talk it through with you.

• Technical report: produce a detailed, well-structured analysis of a technical problem, solution, or system, often with screenshots, code snippets, or configuration evidence. 

• Design or implementation project: plan, build, and document a working prototype, model, or system using industry-standard tools and practices. 

• Case study analysis: evaluate a real or simulated scenario, identify challenges, and propose solutions grounded in computing principles and current best practice. 

• Discussion board contribution: engage in structured, tutor-led online debates, demonstrating critical thinking, problem-solving, and collaboration. 

• Portfolio: compile artefacts such as code, diagrams, system models, and explanatory notes to showcase your technical and professional skills. 

• Ethics or strategy briefing paper: advise an organisation, client, or committee on a complex technology issue, balancing technical, ethical, and commercial perspectives. 

• Presentation or video demonstration: explain your project or technical findings in a recorded or live presentation, sometimes paired with a live or narrated system demo. 

• Research dissertation: complete an in-depth research project that tackles a significant computing challenge, presenting your findings, methodology, and technical recommendations in a formal dissertation.