Artificial Intelligence with Industrial Practice MSc - 2025/6
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Level 7
Final award and programme/pathway title
MSc Artificial Intelligence with Industrial Practice (Placement pathway (24 months))
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time with Placement | PFA71038 | 240 credits and 120 ECTS credits |
QAA Subject benchmark statement (if applicable)
Other internal and / or external reference points
N/A
Faculty and Department / School
Faculty of Engineering and Physical Sciences - Computer Science and Electronic Eng
Programme Leader
SONG Yi-Zhe (CS & EE)
Date of production/revision of spec
22/08/2025
Educational aims of the programme
- The overarching aim of the MSc programme in Artificial Intelligence is to provide a high-quality advanced training in a broad range of core AI topics, including computer vision, natural language processing and audio analysis as well as application-specific topics, such as health and the metaverse; and important regulatory and legal aspects that will critically inform the future development of AI. Students will be able to tailor their learning experience through selection of elective modules to suit their interests and career aspirations. The project dissertation will be chosen by the student allowing them to assist with professional career development within industry or to serve as a precursor to academic research.
- To produce graduates equipped with subject specific knowledge and transferable skills aligned to the Surrey Pillars of graduate attributes and graduates capable of planning and managing their own life-long learning to equip them for roles in industry, in research, in development, in the professions, and/or in public service.
- To provide opportunities for masters students to demonstrate their knowledge, understanding and application of design principles in the creation and development of innovative products to meet a defined need.
- To provide opportunities for masters students to demonstrate their knowledge, understanding and application of engineering concepts and tools in analysis of engineering problems.
- To provide opportunities for masters students to demonstrate their knowledge, understanding and application of mathematical, scientific, and engineering principles.
- To provide opportunities for masters students to demonstrate their knowledge, understanding and application of mathematical, scientific, and engineering principles.
- To provide opportunities for masters students to enhance their digital capabilities through the use of assignments and projects making various use of programming languages as well as enhancing their general transferable information technology skills in the analysis of data, and via the preparation of assignments, reports and presentations.
- To provide opportunities for masters students to enhance their employability skills via use of a training needs analysis which students complete for their individual project to understand how they need to build both their technical and transferable skills.
- To provide opportunities for masters students to enhance their global and cultural intelligence through working with students from around the world and working on a rich variety of assignments and projects appropriate to their programme.
- To provide opportunities for masters students to enhance their knowledge and awareness of sustainability via consideration of sustainability issues such as the UN's Sustainability Development Goals appropriate to their programme.
- To provide opportunities for masters students to enhance their resourcefulness and resilience skills via use of authentic style coursework and assignments, working in teams and undertaking a major individual project. This will build up a student's personal confidence as they advance from well-structured problems to open-ended problems and their individual project.
- To provide students with substantial industry experience through a year-long placement, enabling them to apply theoretical AI knowledge to real-world challenges, develop professional competencies, and gain insight into industrial research and development practices in artificial intelligence applications.
- To enhance students' employability and professional readiness by facilitating extended engagement with industry partners, fostering the development of workplace skills including project management, professional communication, and collaborative problem-solving within authentic AI development environments.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| Apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems in artificial intelligence. | KC | MSc | M1 |
| Formulate and analyse complex problems in artificial intelligence to reach substantiated conclusions. | KC | MSc | M2 |
| Select and apply appropriate computational and analytical techniques to model complex problems in artificial intelligence, discussing the limitations of the techniques employed. | KCT | MSc | M3 |
| Select and critically evaluate technical literature and other sources of information to solve complex problems in artificial intelligence. | CT | MSc | M4 |
| Design solutions for complex problems in artificial intelligence that evidence some originality and meet a combination of societal, user, business and customer needs as appropriate to include consideration of applicable health + safety, diversity, inclusion, cultural, societal, environmental and commercial matters, codes of practice and industry standards. | CP | MSc | M5 |
| Evaluate the environmental and societal impact of solutions to complex problems in artificial intelligence (to include the entire life-cycle of a product or process) and minimise adverse impacts. | CP | MSc | M7 |
| Function effectively as an individual, and as a member or leader of a team. Evaluate effectiveness of own and team performance. | P | MSc | M16 |
| Communicate effectively on complex engineering matters with technical and non technical audiences, evaluating the effectiveness of the methods used. | PT | MSc | M17 |
| Demonstrating the ability to adapt academic knowledge to meet real-world industrial requirements and constraints within a professional work environment while working on substantial AI projects. | KCPT | MSc | M18 |
| Apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems in artificial intelligence. | KC | MSc | M1 |
| Formulate and analyse complex problems in artificial intelligence to reach substantiated conclusions. | KC | MSc | M2 |
| Select and apply appropriate computational and analytical techniques to model complex problems in artificial intelligence, discussing the limitations of the techniques employed. | KCT | MSc | M3 |
| Select and critically evaluate technical literature and other sources of information to solve complex problems in artificial intelligence. | CT | MSc | M4 |
| Design solutions for complex problems in artificial intelligence that evidence some originality and meet a combination of societal, user, business and customer needs as appropriate to include consideration of applicable health + safety, diversity, inclusion, cultural, societal, environmental and commercial matters, codes of practice and industry standards. | P | MSc | M5 |
| Evaluate the environmental and societal impact of solutions to complex problems in artificial intelligence (to include the entire life-cycle of a product or process) and minimise adverse impacts. | P | MSc | M7 |
| Apply knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems in electronic engineering. | KC | MSc | |
| Formulate and analyse problems in electronic engineering to reach substantiated conclusions. | KC | MSc | |
| Select and apply appropriate computational and analytical techniques to model problems in electronic engineering. | KCT | MSc | |
| Select and evaluate technical literature and other sources of information to solve problems in electronic engineering. | CT | MSc |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time with Placement
This Master's Degree programme is studied full-time over two academic years, consisting of 240 credits at FHEQ level 7*. All modules are semester based and worth 15 credits with the exception of project, practice based and dissertation modules.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
*some programmes may contain up to 30 credits at FHEQ level 6.
Programme Adjustments (if applicable)
N/A
Modules
Year 1 (full-time with placement - 2 years) - FHEQ Level 7
Module Selection for Year 1 (full-time with placement - 2 years) - FHEQ Level 7
Students must select two optional modules in Semester 1 and two optional modules in Semester 2
Year 2 (full-time with placement - 2 years) - FHEQ Level 7
| Module code | Module title | Status | Credits | Semester |
|---|---|---|---|---|
| COMM079 | INDUSTRIAL PRACTICE | Compulsory | 60 | Cross Year |
Module Selection for Year 2 (full-time with placement - 2 years) - FHEQ Level 7
Opportunities for placements / work related learning / collaborative activity
| Associate Tutor(s) / Guest Speakers / Visiting Academics | Y | |
| Professional Training Year (PTY) | N | |
| Placement(s) (study or work that are not part of PTY) | Y | Yes |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
Other information
The school/department offering the Artificial Intelligence MSc and Artificial Intelligence MSc with Industrial Placement is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This programme is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Digital capabilities Students on this MSc programme will have many opportunities to enhance their digital capabilities skills via programming and coding exercises making use of the relevant programming languages (such as Python) or modelling languages such as MATLAB. Students will also need to present their findings in the form of an individual and group-based report and presentation using appropriate writing and presentation software.
Employability skills for masters students will be enhanced for those modules that have team activities. Employability is best seen via the preparation and delivery of a major personal project where demonstration and documentation of project deliverables against project objectives will be especially valued by numerate based employers. An opportunity to discuss the project with experts and non-experts during formal evaluation of the project and their peers will be invaluable.
Global and Cultural Capabilities Students on this programme will have an opportunity to engage and work with other masters' students from a range of different regional and cultural backgrounds. Through peer learning and support students will have opportunities to gain appreciation of how engineering is seen and used in different parts of the world. Engineering ethical considerations require students to complete a project Self-Assessment for Governance and Ethics for Human and Data research.
Resourcefulness & Resilience features heavily in the project planning and delivery of their project. Students' resourcefulness and resilience will be enhanced as they will need to think critically and exercise engineering judgment underlying the some of the assumptions they would need to employ in advanced calculations and identify the limitations of those assumptions.
Sustainability Students are exposed to sustainability via choice of components and equipment and need to consider the UN's Sustainability Development Goals in their project work. Sustainability is also to be found in the efficient use of modelling or computational methods to reduce energy.
Industrial Placement Year Students opting for the 24-month programme with placement year will undertake a year-long industrial placement between their taught modules and project. This placement provides invaluable opportunities to apply AI knowledge in professional settings, working on substantial projects within industry, research organizations, or technology companies. The placement year significantly enhances all five Surrey Pillars: students develop advanced Digital Capabilities through hands-on experience with industry-standard AI tools and platforms; strengthen Employability through sustained professional experience and networking; expand Global and Cultural Capabilities by potentially working in international teams or organizations; contribute to Sustainability by engaging with real-world AI applications addressing societal challenges; and build Resourcefulness and Resilience by navigating complex professional environments and adapting to industry pace and requirements. Students will be supported throughout their placement with academic supervision and will complete reflective assessments that integrate their industrial experience with their academic learning.
Quality assurance
The Regulations and Codes of Practice for taught programmes can be found at:
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.