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.

Overseas qualifications may be accepted and will be subject to evidence of equivalency normally verified through ECCTIS (UK ENIC). 

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. 

Your MBA is delivered through an online study platform, where you’ll study either one or two modules at a time depending on your study mode. Full-time students take two modules in parallel (with a short gap between start dates), while part-time students complete one module at a time.

Here’s what you can expect:

• Weekly learning units to guide your progress

• Readings and case studies

• Videos and narrated presentations (mini-lectures)

• Online discussion forums

• Quizzes and tasks to check your understanding

You’ll have the freedom to plan your study time around work and life – but within a guided schedule that helps you stay focused, connected, and on track to succeed.

To get the best learning experience, you’ll need a reliable computer, internet, and audio setup. We recommend a laptop or desktop with at least an Intel i5 processor, 8GB RAM, and 500GB storage (Windows is our primary environment, though you can use Mac or Linux). A stable internet connection (5Mbps download, 2Mbps upload), webcam, and microphone are essential, and we strongly suggest using headphones for online sessions. 

For smoother study, extra resources like a second monitor, noise-cancelling headphones, 16GB+ RAM, SSD storage, and an external hard drive are recommended. Walbrook provides access to the required software, though some programmes may ask you to set up a VPN. Our technical support is Windows-based, but you’re welcome to work on Linux or macOS if you prefer. 

This master's programme combines core computer science topics with advanced artificial intelligence modules. You’ll study software engineering, deep learning algorithms, deep learning, and cloud computing, as well as specialist areas like natural language processing and large language models. The course content blends theoretical knowledge with practical skills so you can tackle real world problems with confidence.

No – you don’t need a degree in computer science to join this programme. Each programme module introduces key concepts early, then develops them into advanced topics like big data analytics, computer architecture, and system development. You’ll have the chance to gain knowledge and develop skills in programming, data analysis, and AI methods as you progress. 

Yes – you’ll explore natural language processing in the Large Language Models and Prompt Engineering module. This is where you’ll gain hands-on experience in prompt design, text processing, and model evaluation. You can also choose to investigate NLP, human factors, or computer vision further in your independent research project. 

Throughout the Master's in Computer Science with Artificial Intelligence programme, you’ll work on software development projects, build secure cloud-based solutions, and apply machine learning algorithms to data. You’ll also strengthen problem solving abilities and learn how to design, test, and deploy intelligent systems that work in practical settings. These skills are highly relevant in a rapidly evolving field like AI. 

Graduates can move into roles such as AI developer, machine learning engineer, or senior AI architect. The broad knowledge you’ll gain – from theoretical concepts to practical aspects of system design – can also lead to potential career paths in software engineering, web development, data analysis, and cloud computing. The programme equips you to work across industries in one of the fastest-growing areas of tech.