Artificial Intelligence MSc - 2023/4
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Levels 6 and 7
Final award and programme/pathway title
MSc Artificial Intelligence
Subsidiary award(s)
| Award | Title |
|---|---|
| PGDip | Artificial Intelligence |
| PGCert | Electronic Engineering |
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time | PFA61043 | 180 credits and 90 ECTS credits |
| Part-time | PFA61044 | 180 credits and 90 ECTS credits |
JACs code
100359
QAA Subject benchmark statement (if applicable)
Engineering (Master)
Other internal and / or external reference points
Uses Engineering Council (EC) document UK-SPEC as benchmark. EC document ¿Accreditation of Higher Education Programmes in Engineering, AHEP4¿; IET Handbook (on the interpretation of EC documents in the context of electronic engineering programmes)
Faculty and Department / School
Faculty of Engineering and Physical Sciences - Computer Science and Electronic Eng
Programme Leader
SONG Yi-Zhe (Elec Elec En)
Date of production/revision of spec
28/03/2023
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 mathematical, scientific, and engineering principles.
- 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 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 practice including the importance of project management, teamwork, and communication within an engineering context.
- 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 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 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 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.
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 |
| Apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems in artificial intelligence | KC | PGDip | M1 |
| Formulate and analyse complex problems in artificial intelligence to reach substantiated conclusions. | KC | PGDip | M2 |
| Select and apply appropriate computational and analytical techniques to model complex problems in artificial intelligence, discussing the limitations of the techniques employed | KCT | PGDip | M3 |
| Select and critically evaluate technical literature and other sources of information to solve complex problems in artificial intelligence | CT | PGDip | 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 | PGDip | 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 | PGDip | M7 |
| Apply knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems in electronic engineering. | KC | PGCert | |
| Formulate and analyse problems in electronic engineering to reach substantiated conclusions. | KC | PGCert | |
| Select and apply appropriate computational and analytical techniques to model problems in electronic engineering | KCT | PGCert | |
| Select and evaluate technical literature and other sources of information to solve problems in electronic engineering | CT | PGCert | |
| Design solutions for problems in electronic engineering 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 | PGCert |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time
This Master's Degree programme is studied full-time over one academic year, consisting of 180 credits at FHEQ level 6 and 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)
Part-time
This Master's Degree programme is studied part-time over three to five years, consisting of 180 credits at FHEQ level 6 and 7. All modules are 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)
Programme Adjustments (if applicable)
N/A
Modules
Year 1 - FHEQ Levels 6 and 7
Module Selection for Year 1 - FHEQ Levels 6 and 7
One optional module in semester 1
Two optional modules in semester 2
Unstructured (3-5 years) PT - FHEQ Levels 6 and 7
Module Selection for Unstructured (3-5 years) PT - FHEQ Levels 6 and 7
One optional modules in semester 1
Two optional modules in semester 2
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) | N | |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
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 2023/4 academic year.