Why study this course?
Artificial Intelligence is transforming industries worldwide, creating demand for professionals who can develop, deploy, and critically evaluate AI technologies. This flexible online programme enables you to gain advanced AI knowledge while balancing work and personal commitments. Study at your own pace and learn from leading researchers in a programme that combines rigorous academic foundations with practical skills for real-world impact.
This online programme allows you to study flexibly, choosing from MSc, PGDip or PGCert pathways, and progressing at a pace that fits your life.
On this Artificial Intelligence online course, you will:
- Master the foundations of Artificial Intelligence and Machine Learning through hands-on, industry-relevant study.
- Develop the programming and analytical skills needed to create intelligent systems and AI-driven applications.
- Learn how to build, evaluate, and deploy machine learning models that deliver real-world impact.
- Apply optimisation and automation techniques to address complex challenges across a range of domains.
- Build expertise in cutting-edge AI areas, including computer vision and natural language processing.
- Develop the knowledge and critical awareness needed to design, evaluate, and apply AI technologies responsibly.
Teaching
Lectures, seminars, tutorials, and practical work.
Assessment
A mix of coursework exercises, presentations, and tests.
Dissertation project
MSc students will submit a software artefact and a detailed description of its context in Artificial Intelligence.
Schedule
You will access modules and components at a pace and on a timetable that suits your work and study environment.
Modules
Those studying towards an MSc or PGDip take the compulsory modules and one optional module. MSc students also undertake a dissertation project.
Those studying for a PGCert take four modules.
Course information may change. Module information and course content, teaching and assessment may change each year and after you have accepted your offer to study at the University of St Andrews. We display the most up-to-date information possible, but this could be from a previous academic year. For the latest module information, see the module catalogue.
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- Machine Learning Algorithms: covers the essential theory and algorithms, including mathematical foundations, and methodological approaches, using a variety of regression, classification and unsupervised approaches.
- End-to-End Machine Learning: focuses on using Python packages to perform end-to-end data-driven analyses.
- Programming in Python: introduces and revises modelling, design and implementation in Python.
- Discrete Optimisation: covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimisation.
- Automated Machine Learning: focuses on methods and techniques to automatically optimise machine learning solutions.
- Computer Vision: provides a rigorous introduction to modern computer vision, including image formation, feature extraction, deep learning, and contemporary vision architectures.
- Natural Language Processing: covers the principles and methods that enable computers to understand, generate and interact using human language.
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- Data and Information Visualisation: focuses on the question of how to utilise visual representations to make information accessible for exploration and analysis.
- Complex Systems Modelling and Simulation: introduces a range of techniques and their applications to different classes of problems, with a practical focus on modern network-based models and simulation.
- Data-Driven Systems: is an advanced research-focused module that presents the foundations of distributed systems and techniques that process data.
- Research Methods in Data Science: introduces the skills necessary for the planning, data gathering, data analysis and dissemination stages of Data Science research.
- Numeric Optimisation: takes linear algebra and optimisation as the primary topics of interest and solutions to machine learning problems as the applications of this of the resulting tools, techniques and algorithms.
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Students will submit a dissertation in Artificial Intelligence, comprising a detailed software artefact that implements and evaluates a workflow and a detailed description of the artefact and its context in the area of study. This module involves regular one-to-one contact with the Academic Supervisor.
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- Machine Learning Algorithms: covers the essential theory and algorithms, including mathematical foundations, and methodological approaches, using a variety of regression, classification and unsupervised approaches.
- End-to-End Machine Learning: focuses on using Python packages to perform end-to-end data-driven analyses.
- Programming in Python: introduces and revises modelling, design and implementation in Python.
- Discrete Optimisation: covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimisation.
- Automated Machine Learning: focuses on methods and techniques to automatically optimise machine learning solutions.
- Computer Vision: provides a rigorous introduction to modern computer vision, including image formation, feature extraction, deep learning, and contemporary vision architectures.
- Natural Language Processing: covers the principles and methods that enable computers to understand, generate and interact using human language.
What it will lead to
Careers
This programme will prepare you for careers at the forefront of Artificial Intelligence. Graduates will develop the technical skills and critical thinking sought by employers across a range of industries.
Typical careers include:
- AI programmer
- machine learning engineer
- data scientist
- full-stack AI developer
- data analyst
- business intelligence analyst
- AI consultant
The programme also provides a strong foundation for further postgraduate research and doctoral study in AI and related disciplines.
Why St Andrews?
The School of Computer Science is highly rated for its theoretical and practical research in areas such as:
- AI
- symbolic computation
- networking
- distributed systems
- human computer interaction
- systems engineering
Get to know us
Join our optional 'Transition to CS@St Andrews' online hub to get early information about learning, teaching, assessment and student support. You can chat with staff and current students during live monthly Question and Answer sessions over the summer before the start of your course.
Alumni
Whether you join us online or in person, when you graduate you become a member of the University's worldwide alumni community. Benefit from access to alumni clubs, the Saint Connect networking and mentoring platform, and careers support.
Wherever you are, you can take St Andrews with you. Online Masters at the University of St Andrews combine all the benefits of studying at one of the world's oldest and best universities, with all the advantages of flexible, personalised learning.
“The flexible online learning format makes it easy to study alongside full-time work. The materials are excellent, the lecturers are approachable and give detailed feedback on assignments. It’s a challenging and rewarding experience.”
Entry requirements
For entry onto the MSc: A 2.1 undergraduate Honours degree in any subject from the UK or the equivalent international qualification.
We will also consider applicants who do not have an undergraduate degree. In these circumstances we expect candidates to have at least five years of relevant professional learning. The Admissions team will holistically assess your application and determine the best route of entry for you. In some cases, this may be onto the PGCert in the first instance, from which students who attain a certain level in their modules will have the opportunity to progress to a full Masters degree.
The qualifications listed are indicative of standard requirements for entry. Some academic Schools may require applicants to achieve higher grades than the standard to be competitive. Obtaining the listed entry requirements will not guarantee you a place, as the University takes a holistic view of every application.
If your qualification does not appear in the list, it may not meet the typical requirements for entry to this programme. You may need a higher-level qualification to be competitive, for example, a Masters degree.
English language requirements
Some applicants may need to provide an English language test score to evidence their English language ability. Find out who needs to provide evidence of English language ability. Details of acceptable tests and scores are provided below.
Test scores are only normally accepted if obtained within two years of the programme start date.
Application requirements
- A CV or résumé. This should include your personal details with a history of your education and employment to date.
- A personal statement explaining:
- why you have applied for this course
- how it relates to your personal or professional ambitions
- how your academic and professional background show you have the skills needed to work effectively at postgraduate level.
- One original signed academic reference. An employment reference is acceptable for applicants unable to provide an academic reference.
- Academic transcripts and degree certificates.
Fees and funding
£1,500 per 15 credits with an estimated total fee of £18,000
Scholarships and funding
We are committed to supporting you through your studies, regardless of your financial circumstances. You may be eligible for scholarships, discounts or other support:
Contact us
- Postgraduate online information events
- The School can help with course content, teaching and other topics: ask the School
- Ask University Admissions about how to apply, fees, scholarships and other topics
Start your journey
Legal notices
Admission to the University of St Andrews is governed by our Admissions policy
Information about all programmes from previous years of entry can be found in the course archive.
Curriculum development
As a research intensive institution, the University ensures that its teaching references the research interests of its staff, which may change from time to time. As a result, programmes are regularly reviewed with the aim of enhancing students' learning experience. Our approach to course revision is described online.
Tuition fees
The University will clarify compulsory fees and charges it requires any student to pay at the time of offer. The offer will also clarify conditions for any variation of fees. The University’s approach to fee setting is described online.
Page last updated: 3 November 2026