Artificial Intelligence MSc - 2022/3
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 |
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 (CS & EE)
Date of production/revision of spec
12/09/2023
Educational aims of the programme
- The taught postgraduate Degree Programmes of the School of Computer Science and Electronic Engineering are designed to aid with professional career development within the relevant industry and to serve as a precursor to academic research. Our philosophy is to integrate the acquisition of core engineering and scientific knowledge with the development of key practical and transferrable skills.
- The programme will enhance student employability, resilience and resourcefulness, global and cultural capabilities, sustainability awareness, and digital capabilities (the five pillars of the University of Surrey¿s Curriculum Framework) through a range of appropriate, contemporary, and engaging pedagogical methods
- Employment Context: A graduate from this MSc Programme should be aware of commercial, industrial and employment-related practices and issues likely to affect his/her activities around AI.
- Engineering problem solving: A graduate from this MSc Programme should be able to analyse problems within the field of artificial intelligence, and more broadly in computer science and engineering, and find solutions.
- A graduate from this MSc Programme should be able to use relevant workshop and laboratory tools and equipment and have experience of using relevant task-specific software packages to perform AI tasks.
- Societal and environmental context: A graduate from this MSc Programme should be aware of the societal, environmental, ethical and regulatory context of his/her AI activities.
- A graduate from this MSc Programme should know, understand and be able to use the basic mathematical, scientific and engineering facts and principles associated with the topics with AI and be aware of the legal and societal context of the topic
- A graduate from this MSc Programme should know, understand and be able to use the basic mathematical, scientific and engineering facts and principles associated with the topics with AI and be aware of the legal and societal context of the topic.
- A graduate from this MSc Programme should know, understand and be able to use the basic mathematical, scientific and engineering facts and principles associated with the topics with AI and be aware of the legal and societal context of the topic
- Provide students with an extensive choice of taught modules, in subjects for which the School has an international and UK research reputation.
- Develop students' understanding of the underlying science, engineering, and technology around AI, and enhance their ability to relate this to industrial practice.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| Represent complex problems in AI and image processing as mathematical models and analyse these problems applying appropriate algorithmic machine learning and optimization techniques to solve those problems. | KCP | M1, M2, M3 | |
| Design solutions for complex problems that evidence some originality and meet a combination of societal, user, business and customer needs in AI. | KCPT | M5 | |
| Evaluate environmental, societal, ethical, governance and regulatory issues associated with artificial intelligence in society. | KCPT | M7 | |
| Understand and apply state-of-the-art, image processing, VR and AR, speech recognition in the modern world, and open challenges. | K | M3 | |
| Select and critically evaluate technical literature and other sources of information to solve complex problems in AI | CT | M4 | |
| Evaluate technical literature and other sources of information to solve complex problems in AI | CT | M4 | |
| Evaluate technical literature and other sources of information to solve problems in Electronic Engineering | CT | M4 | |
| Teamwork: Functional effectively as an individual and as a member of or leader of a team. Evaluate effectiveness of own and team performance | KT | M16 | |
| Communication: Communicate effectively on complex engineering matters with technical and non-technical audiences, evaluating the effectiveness of the methods used. | KT | M17 |
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 2022/3 academic year.