MSc — 2027 entry Artificial Intelligence (Conversion)
AI is reshaping every profession and creating new ones. Designed for graduates from any discipline, our Artificial Intelligence (Conversion) MSc gives you a holistic command of AI: the technical foundation, ethical grounding and fluency to thrive in your own field, broaden into others, or step into new AI careers.
7,023+ people have created a bespoke digital prospectus
Why choose
this course?
- Our Artificial Intelligence (Conversion) MSc is designed for graduates and professionals from non-technical backgrounds who want to confidently apply AI in their own profession and build future-ready skills without needing a computer science or advanced maths degree.
- Develop expertise in how AI is transforming different sectors through our AI+X approach, exploring real-world applications across business, healthcare, the creative industries and public services, so you can lead innovation within your own discipline.
- Join the Surrey Institute for People-Centred AI (PAI), one of the largest AI research environments in Europe, with Surrey ranked No. 1 in the UK for computer vision.
- Develop in-demand technical skills alongside critical understanding of People-Centred AI, where ethics, regulation and the societal impacts of AI are embedded from day one.
- Gain hands-on experience with modern AI tools and workflows used in industry, including AI assisted coding and development practices, to build practical applied capability.
- We’re preparing you for the future by integrating AI into every course, building digital skills, confidence and creativity that employers value in tomorrow’s workplace.
Statistics
1st in the UK
And 4th in Europe for Computer Vision in the Computer Science Rankings (CS Rankings, 2025)
What you will study
You’ll begin with four core modules designed to build your technical understanding of AI alongside ethical and societal perspectives.
You’ll then choose four AI+X electives from an expanding portfolio. AI+X modules explore how AI is being applied across different industries and disciplines, helping you understand both the opportunities and challenges AI presents across the wider economy.
Our flexible course structure gives you the freedom to apply AI within your existing professional field, explore new sectors, or combine interests across disciplines. You’ll also complete an individual dissertation project supervised by a leading academic at the Surrey Institute for People-Centred AI (PAI), giving you the opportunity to investigate a topic linked to your professional interests that directly supports your future career or research ambitions.
Why AI+X?
Artificial Intelligence is no longer confined to computer science; its impact now spans every sector. This programme is designed for professionals working in fields being reshaped by AI; from healthcare and law, to business, the creative industries and public services. You build a rigorous foundation in AI through core study in programming, machine learning, ethics and regulation, and People-Centred AI, with a focus on understanding both technical principles and their wider implications.
You then progress to AI+X electives. These explore how AI is being applied across different industries and disciplines, enabling you to analyse its impact within and beyond your sector. Electives are co-developed with academic experts from across the University and may include areas such as AI and the creative industries, cybersecurity, sustainability, life sciences and public health.
This flexible structure allows you to apply AI in your own field, broaden into new areas, or do both.
The Surrey Institute for People-Centred AI
The Surrey Institute for People-Centred AI (PAI) brings together expertise from across the University to explore how AI can be developed and applied responsibly across sectors including healthcare, business, law, education, media and the creative industries.
As a student, you will be part of vibrant interdisciplinary community, one of the largest AI research environments in Europe. Working closely with industry and end-users, PAI co-designs solutions that address real-world challenges. You will have regular opportunities to engage with cutting-edge research through guest lecturers, applied case studies and dissertation projects.
Surrey is ranked No. 1 in the UK for computer vision, with nearly four decades of world-leading research in image processing, machine learning, deep learning and audio-visual AI. You’ll also benefit from access to specialist research facilities, including advanced GPU computing infrastructure, supporting you to tackle computationally intensive projects with confidence. Students may also engage with major collaborative initiatives and live research challenges in PAI, giving you valuable experience and insight. Some of the current flagship programmes include:
- AI4ME: a partnership with the BBC focused on AI-driven personalised media
- The CoSTAR National Lab: a UK government investment in creative-technology with Surrey as a founding partner
- The UKRI Centre for Doctoral Training in AI for Digital Media Inclusion, led by Surrey in collaboration with Royal Holloway, University London
- DECaDE: Surrey’s Digital Economy research centre exploring AI and Blockchain.
PAI also works with industry partners including Samsung, Google, the BBC and a growing network of creative-tech and health-tech firms whose practitioners contribute guest lectures and dissertation co-supervision.
180 credits at FHEQ Level 7. The full-time route runs over 12 months; a part-time route is available over 24–60 months with flexible module sequencing for working professionals.
- Semester 1: Four compulsory core modules (60 credits)
- Semester 2: Four AI+X electives chosen from a portfolio of ten (60 credits), aligning learning with your prior domain expertise
- Summer: A 60-credit year-long individual dissertation, typically situated in the student’s own domain.
Exit awards: PGCert (60 credits) and PGDip (120 credits) are available for students who do not complete the full MSc.
The structure of our programmes follows clear educational aims that are tailored to each programme. These are all outlined in the programme specifications which include further details such as the learning outcomes:
Modules
Modules listed are indicative, reflecting the information available at the time of publication. Modules are subject to teaching availability, student demand, and/or class size caps.
The University operates a credit framework for all taught courses based on a 15-credit tariff, meaning all modules are comprised of multiples of 15 credits.
Please note that modules are listed by the academic calendar and are reflective of a September start date. For students that start a programme in February, please note that the modules listed as semester two will take place in the spring term and modules listed as semester one will take place in the autumn term.
Course options
Year 1
Semester 1
Compulsory
This module provides a solid foundation in Python programming relevant for data science. It introduces students to core programming concepts, essential Python libraries, and practical coding techniques widely used in data analysis and machine learning. By the end of the module, students will be confident in writing Python programs for solving real-world data problems, handling data, performing analysis, and creating visualisations.
View full module detailsThis module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.
View full module detailsThis module offers an introduction to the use of AI in society, work, media and communication, government and policy. It puts people ¿ as opposed to technology -- in the centre of AI, and highlights core considerations in planning for AI applications as a response to issues and considerations in the society. This is done by exploring varied positions of users and stakeholders in relation to AI, examining the suitability of AI and associated tools and methods to the productivity/progress/propagation in society. In this module we discuss the opportunities, but also the challenges, risks, threats and ethical implications involved in the use of AI in society.
View full module detailsThis module introduces students to the foundations of modern machine learning and AI. It is designed to be accessible to students from any disciplinary background, with no prior mathematics, programming, or technical preparation assumed. The pedagogical approach is informed by accessible, intuition-first learning resources that demonstrate AI fundamentals can be taught to non-specialist learners without requiring formal mathematical preparation or coding background. By the end of the module, students have a working conceptual grasp of how machine learning and modern AI systems behave - sufficient to engage critically with applied AI topics across a range of domains.
View full module detailsSemester 2
Optional
Cyber security is one of the major challenges for computer and information systems. Many users have lost data due to viruses, both on home and business computers. Most of us have seen a range of email messages attempting different kinds of fraud. Vulnerabilities are everywhere. Some are obvious or well-known; others are obscure and harder to spot. Security is not limited to secrecy and confidentiality, but also involves problems like integrity, availability, and effectiveness of information. Moreover, security issues can potentially affect all of us, from innocent home users to companies and even governments. Cyber security is not just a technical problem but needs to be embedded throughout an organisation to be effective. As such good security solutions build on a complete understanding of the values at stake, and the supporting business processes and requirements. This includes people as well as information systems and physical resources. Consequently, raising security awareness and embedding security within roles and policies is as important, if not more, as secure software. In short, secure solutions can only be implemented with both key technical skills and a solid understanding of cultures and people skills. The module looks broadly at the implications of regulations on the cyber crime landscape. It also brings together the issues of cyber from a high-level perspective. It will also equip students with an awareness of what is very current out there in terms of leading-edge applications of cyber security. The module uniquely accesses leading international speakers in their field, draws on the significant expertise of leading industrialists and through shaping the module into distinct learning blocks we are also able to ensure that our students will be directed to key industry reports, regulations and best practice guidance that shapes current industry thinking in cyber security for business and government.
View full module detailsRecently, Artificial Intelligence (AI) has been playing a key role in the research and development of scientific and technological breakthroughs in many disciplines to solve real world problems, providing new foundations and steppingstones to foster more advances and solutions. In this context, AI has a great potential to play a transformative role in helping to achieve the United Nations Sustainable Development Goals (UNSDG), by providing new insights, enabling more efficient use of resources, and supporting a better understanding of complex systems that underpin the dynamics of people's lives and the planet's environment. Therefore, the purpose of this module is to present the key concepts with practical applications related to the development of more sustainable AI techniques (e.g. model, data and energy efficiency, bias and unfairness identification and mitigation, trustworthy AI, physics-informed neural networks etc.), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing etc.).
View full module detailsThe module provides students with advanced knowledge about how Artificial Intelligence (AI) can be used for facilitating multilingual communication. The module explores how Natural Language Processing (NLP) and Machine Translation (MT) facilitate multilingual communication and presents different AI-based paradigms for producing multilingual content. Students gain hands-on experience in how to train translation engines, and how the output of these engines should be evaluated. The role of Large Language Models (LLMs) in workflows that produce multilingual content is also explored. In addition, students use LLMs for processing of multimodal input in order to produce captions for images, audio description for videos and to generate natural-sounding speech. The module enables students to understand the challenges faced when using artificial intelligence for multilingual communication and translation, regardless of whether they are processing text, speech or video. The focus is on enhancing students¿ digital capabilities, especially those linked to the language services industry and multilingual communication workflows. Students gain a thorough understanding of the role of data in AI-based multilingual communication workflows by exploring how to use corpora to learn the characteristics of a language, harvest relevant data from the web, annotate it with relevant information, and use it for tuning AI-based tools. Examples of practical tasks in dedicated practical sessions include text classification (e.g. sentiment analysis, harmful text filtering), development of chatbots and fine-tuning of MT engines. These practical tasks aim at improving students¿ problem-solving skills and contribute to their future career development. Knowledge of programming is not necessary, but students who have a programming background are offered the opportunity to use this knowledge during the module. While TRAM511 is not a prerequisite, students taking this module are expected to have a good understanding of AI in general.
View full module detailsSemester 1 & 2
Core
Expected prior learning: Appropriate background knowledge related to the project topic. Module purpose: This is an individual student project module giving each masters student an opportunity to gain realistic experience in developing a solution to a problem from its inception to a demonstrable result. It provides a framework as well as a vehicle for exercising all key aspects of project work, from project specification, through literature and technology research, leading to project planning, problem solving as well as design and implementation, culminating in performance assessment, project demonstration, and project evaluation. It also provides a scope for gaining practical experience interpersonal skills, use of IT, project management, project reporting and project presentation. The project can be either of engineering design nature or have a research flavour. This module is complementary to all other taught modules in order to apply the learning gained into undertaking an independent piece of research and/or development.
View full module detailsModule selection for Year 1 (full-time) - FHEQ Level 7
Four compulsory modules in Semester 1
Four optional AI+X elective modules (from six) in Semester 2
As part of the approval process the following new modules have been developed and will be added to the programme once available:
AI for Life Sciences
AI + Creative Industries
AI and Digital Transformation for Public Health
Year 1
Semester 1
Compulsory
This module provides a solid foundation in Python programming relevant for data science. It introduces students to core programming concepts, essential Python libraries, and practical coding techniques widely used in data analysis and machine learning. By the end of the module, students will be confident in writing Python programs for solving real-world data problems, handling data, performing analysis, and creating visualisations.
View full module detailsThis module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.
View full module detailsThis module offers an introduction to the use of AI in society, work, media and communication, government and policy. It puts people ¿ as opposed to technology -- in the centre of AI, and highlights core considerations in planning for AI applications as a response to issues and considerations in the society. This is done by exploring varied positions of users and stakeholders in relation to AI, examining the suitability of AI and associated tools and methods to the productivity/progress/propagation in society. In this module we discuss the opportunities, but also the challenges, risks, threats and ethical implications involved in the use of AI in society.
View full module detailsThis module introduces students to the foundations of modern machine learning and AI. It is designed to be accessible to students from any disciplinary background, with no prior mathematics, programming, or technical preparation assumed. The pedagogical approach is informed by accessible, intuition-first learning resources that demonstrate AI fundamentals can be taught to non-specialist learners without requiring formal mathematical preparation or coding background. By the end of the module, students have a working conceptual grasp of how machine learning and modern AI systems behave - sufficient to engage critically with applied AI topics across a range of domains.
View full module detailsSemester 2
Optional
The module provides students with advanced knowledge about how Artificial Intelligence (AI) can be used for facilitating multilingual communication. The module explores how Natural Language Processing (NLP) and Machine Translation (MT) facilitate multilingual communication and presents different AI-based paradigms for producing multilingual content. Students gain hands-on experience in how to train translation engines, and how the output of these engines should be evaluated. The role of Large Language Models (LLMs) in workflows that produce multilingual content is also explored. In addition, students use LLMs for processing of multimodal input in order to produce captions for images, audio description for videos and to generate natural-sounding speech. The module enables students to understand the challenges faced when using artificial intelligence for multilingual communication and translation, regardless of whether they are processing text, speech or video. The focus is on enhancing students¿ digital capabilities, especially those linked to the language services industry and multilingual communication workflows. Students gain a thorough understanding of the role of data in AI-based multilingual communication workflows by exploring how to use corpora to learn the characteristics of a language, harvest relevant data from the web, annotate it with relevant information, and use it for tuning AI-based tools. Examples of practical tasks in dedicated practical sessions include text classification (e.g. sentiment analysis, harmful text filtering), development of chatbots and fine-tuning of MT engines. These practical tasks aim at improving students¿ problem-solving skills and contribute to their future career development. Knowledge of programming is not necessary, but students who have a programming background are offered the opportunity to use this knowledge during the module. While TRAM511 is not a prerequisite, students taking this module are expected to have a good understanding of AI in general.
View full module detailsModule selection for Year 1 (part-time) - FHEQ Level 7
As part of the approval process the following new modules have been developed and will be added to the programme once available:
AI and Digital Transformation for Public Health Year 1 or Year 2 Semester 2
Year 2
Semester 1
Compulsory
This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.
View full module detailsThis module offers an introduction to the use of AI in society, work, media and communication, government and policy. It puts people ¿ as opposed to technology -- in the centre of AI, and highlights core considerations in planning for AI applications as a response to issues and considerations in the society. This is done by exploring varied positions of users and stakeholders in relation to AI, examining the suitability of AI and associated tools and methods to the productivity/progress/propagation in society. In this module we discuss the opportunities, but also the challenges, risks, threats and ethical implications involved in the use of AI in society.
View full module detailsSemester 2
Optional
Cyber security is one of the major challenges for computer and information systems. Many users have lost data due to viruses, both on home and business computers. Most of us have seen a range of email messages attempting different kinds of fraud. Vulnerabilities are everywhere. Some are obvious or well-known; others are obscure and harder to spot. Security is not limited to secrecy and confidentiality, but also involves problems like integrity, availability, and effectiveness of information. Moreover, security issues can potentially affect all of us, from innocent home users to companies and even governments. Cyber security is not just a technical problem but needs to be embedded throughout an organisation to be effective. As such good security solutions build on a complete understanding of the values at stake, and the supporting business processes and requirements. This includes people as well as information systems and physical resources. Consequently, raising security awareness and embedding security within roles and policies is as important, if not more, as secure software. In short, secure solutions can only be implemented with both key technical skills and a solid understanding of cultures and people skills. The module looks broadly at the implications of regulations on the cyber crime landscape. It also brings together the issues of cyber from a high-level perspective. It will also equip students with an awareness of what is very current out there in terms of leading-edge applications of cyber security. The module uniquely accesses leading international speakers in their field, draws on the significant expertise of leading industrialists and through shaping the module into distinct learning blocks we are also able to ensure that our students will be directed to key industry reports, regulations and best practice guidance that shapes current industry thinking in cyber security for business and government.
View full module detailsRecently, Artificial Intelligence (AI) has been playing a key role in the research and development of scientific and technological breakthroughs in many disciplines to solve real world problems, providing new foundations and steppingstones to foster more advances and solutions. In this context, AI has a great potential to play a transformative role in helping to achieve the United Nations Sustainable Development Goals (UNSDG), by providing new insights, enabling more efficient use of resources, and supporting a better understanding of complex systems that underpin the dynamics of people's lives and the planet's environment. Therefore, the purpose of this module is to present the key concepts with practical applications related to the development of more sustainable AI techniques (e.g. model, data and energy efficiency, bias and unfairness identification and mitigation, trustworthy AI, physics-informed neural networks etc.), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing etc.).
View full module detailsThe module provides students with advanced knowledge about how Artificial Intelligence (AI) can be used for facilitating multilingual communication. The module explores how Natural Language Processing (NLP) and Machine Translation (MT) facilitate multilingual communication and presents different AI-based paradigms for producing multilingual content. Students gain hands-on experience in how to train translation engines, and how the output of these engines should be evaluated. The role of Large Language Models (LLMs) in workflows that produce multilingual content is also explored. In addition, students use LLMs for processing of multimodal input in order to produce captions for images, audio description for videos and to generate natural-sounding speech. The module enables students to understand the challenges faced when using artificial intelligence for multilingual communication and translation, regardless of whether they are processing text, speech or video. The focus is on enhancing students¿ digital capabilities, especially those linked to the language services industry and multilingual communication workflows. Students gain a thorough understanding of the role of data in AI-based multilingual communication workflows by exploring how to use corpora to learn the characteristics of a language, harvest relevant data from the web, annotate it with relevant information, and use it for tuning AI-based tools. Examples of practical tasks in dedicated practical sessions include text classification (e.g. sentiment analysis, harmful text filtering), development of chatbots and fine-tuning of MT engines. These practical tasks aim at improving students¿ problem-solving skills and contribute to their future career development. Knowledge of programming is not necessary, but students who have a programming background are offered the opportunity to use this knowledge during the module. While TRAM511 is not a prerequisite, students taking this module are expected to have a good understanding of AI in general.
View full module detailsSemester 1 & 2
Core
Expected prior learning: Appropriate background knowledge related to the project topic. Module purpose: This is an individual student project module giving each masters student an opportunity to gain realistic experience in developing a solution to a problem from its inception to a demonstrable result. It provides a framework as well as a vehicle for exercising all key aspects of project work, from project specification, through literature and technology research, leading to project planning, problem solving as well as design and implementation, culminating in performance assessment, project demonstration, and project evaluation. It also provides a scope for gaining practical experience interpersonal skills, use of IT, project management, project reporting and project presentation. The project can be either of engineering design nature or have a research flavour. This module is complementary to all other taught modules in order to apply the learning gained into undertaking an independent piece of research and/or development.
View full module detailsModule selection for Year 2 (part-time) - FHEQ Level 7
As part of the approval process the following new modules have been developed and will be added to the programme once available:
AI for Life Sciences Year 2 Semester 2
AI + Creative Industries Year 2 Semester 2
Teaching and learning
Students learn from PAI academic staff, complemented by practitioner-led guest lectures from PAI’s industry partner network and embedded case studies from active flagship research programmes.
- Group work
- Independent study
- Laboratory work
- Lectures
- Online learning
- Practical sessions
- Project work
- Research work
- Tutorials
- Workshops
Assessment
Assessment is varied across modules and deliberately authentic. Students build and evaluate AI systems on realistic datasets, write technical reports in industry-standard formats, and defend their dissertation orally. The mix includes coursework, written examinations and portfolios.
The programme takes a transparent and practical approach to generative AI. Students are encouraged to use AI tools responsibly in coursework, provided use is clearly declared and understanding can be demonstrated in oral assessment. Written examinations remain closed-book and AI-free, focusing on core conceptual understanding. Lab work and dissertations include reflective elements on how AI tools were used and where human judgement was essential.
General course information
Contact hours
Contact hours can vary across our modules. Full details of the contact hours for each module are available from the University of Surrey's module catalogue. See the modules section for more information.
Timetable
New students will receive their personalised timetable during Welcome Week. In later semesters, at least one week before the start of the semester.
Scheduled teaching can take place on any day of the week (Monday – Friday), with part-time classes normally scheduled for one or two days. Wednesday afternoons tend to be for sports and cultural activities.
View our code of practice for the scheduling of teaching and assessment (PDF) for more information.
Location
This course is based at Stag Hill campus. Stag Hill is the University's main campus and where the majority of our courses are taught.
We offer careers information, advice and guidance to all students whilst studying with us, which is extended to our alumni for three years after leaving the University.
AI skills are associated with higher earning potential in the UK labour market. AI-related roles advertise at a median salary of approximately £62,700, around 42% higher than wider IT roles (Perspective Economics, 2026).
Demand for these skills is also widespread, with a DSIT-commissioned survey reporting that 97% of UK organisations identify at least one AI-related skills shortage (DSIT, 2025).
Graduates are prepared for roles such as
- AI Product Manager / AI Strategy Lead
- AI Consultant or Digital Transformation Specialist
- Responsible AI / AI Governance professional
- Domain AI Specialist (applying AI within sectors such as healthcare, law, business or the creative industries)
- AI-enabled professional roles across clinical, public sector, and industry contexts
- Doctoral study in applied AI or related interdisciplinary fields.
Industry connections
Graduates also benefit from Surrey’s AI research and industry community, including links through the Surrey Institute for People-Centred AI (PAI), supporting engagement with organisations across the creative technology, healthcare, financial services and public sector AI landscape.
Chaitat U.
Graduate - Artificial Intelligence MSc
Yash Kulthe
Student - Artificial Intelligence MSc
UK qualifications
A minimum of a 2:2 UK honours degree, or a recognised equivalent international qualification.
English language requirements
IELTS Academic: 6.5 overall including 6.0 in each category.
These are the English language qualifications and levels that we can accept.
If you do not currently meet the level required for your programme, we offer intensive pre-sessional English language courses, designed to take you to the level of English ability and skill required for your studies here.
Credit Transfer and Recognition of Prior Learning
We recognise that many students enter their course with valuable knowledge and skills developed through a range of ways.
If this applies to you, the recognition of prior learning process may mean you can join a course without the formal entry requirements, or at a point appropriate to your previous learning and experience.
There are restrictions on some courses, and fees may be payable for certain claims. Please contact the Admissions team with any queries.
Study and work abroad
As a student on this programme, you’ll benefit from engagement with Surrey’s leading research and innovation activity including initiatives such as the BBC AI4ME EPSRC Prosperity Partnership, the CoSTAR National Lab, and the UKRI CDT in AI for Digital Media Inclusion.
This engagement is delivered through a combination of guest lectures, applied case studies embedded in taught modules, and opportunities for supervised dissertation projects. Where appropriate, you may also have access to placement and study abroad opportunities, including schemes such as the Turing scheme, subject to availability and final confirmation prior to the start of the programme.
Scholarships and bursaries
Discover what scholarships and bursaries are available to support your studies.
Fees per year
Explore UKCISA’s website for more information if you are unsure whether you are a UK or overseas student. View the list of fees for all postgraduate courses.
Fee options
- UK
- £13,400
- Overseas
- £17,700
- UK
- £6,700
- Overseas
- £8,900
- Fees for part-time routes are applicable in each year of the course
- These fees apply to the academic year 2027-28 only. Fees are reviewed annually, and tuition fees may increase for courses running over more than one year.
Read our tuition fees guidance to find out more about payment schedules and how to pay.
Additional costs
Find out more about additional costs that may be incurred during your time at Surrey.
Funding
You may be able to borrow money to help pay your tuition fees and support you with your living costs. Find out more about postgraduate student finance.
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Register your interestAdmissions information
Once you apply, you can expect to hear back from us within 14 days. This might be with a decision on your application or with a request for further information.
Our code of practice for postgraduate taught admissions explains how the Admissions team considers applications and admits students. Read our postgraduate applicant guidance for more information on applying.
About the University of Surrey
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Contact our Admissions team or talk to a current University of Surrey student online.
Terms and conditions
When you accept an offer to study at the University of Surrey, you will be agreeing to follow our policies and procedures, student regulations, and terms and conditions.
We provide these terms and conditions at the offer stage. You will be asked to accept these when you accept the offer made to you. You will be provided with these terms and conditions again at registration by way of reminder.
Disclaimer
This online prospectus has been published in advance of the academic year to which it applies.
Whilst we have done everything possible to ensure this information is accurate; some changes may happen between publishing and the start of the course.