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

1. UK Standard for Professional Engineering Competence and Commitment (UK-SPEC, Engineering Council, August 2020) and associated Accreditation of Higher Education Programmes, version 4 (AHEP4, August 2020). 2. QAA Subject Benchmark Statement for Engineering (March 2023). 3. Academic Accreditation Information Pack for Higher Education Institutions, Institution of Engineering Technology (accessed 2023).

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

01/12/2023

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

Unstructured (3-5 years) PT - FHEQ Levels 6 and 7

Module code Module title Status Credits Semester
EEE3032 COMPUTER VISION AND PATTERN RECOGNITION Optional 15 1
EEEM004 60 CREDIT STANDARD PROJECT Core 60 Year-long
EEEM005 AI AND AI PROGRAMMING Optional 15 2
EEEM030 SPEECH & AUDIO PROCESSING & RECOGNITION Optional 15 1
LAWM161 ETHICS AND REGULATION OF ARTIFICIAL INTELLIGENCE Compulsory 15 1
MANM115 INNOVATION MANAGEMENT Optional 15 2
EEEM071 ADVANCED TOPICS IN COMPUTER VISION AND DEEP LEARNING Compulsory 15 2
EEEM068 APPLIED MACHINE LEARNING Compulsory 15 2
EEEM066 FUNDAMENTALS OF MACHINE LEARNING Compulsory 15 1
EEEM067 AR, VR AND THE METAVERSE Optional 15 1
EEEM069 AI AND HEALTH Optional 15 2
MANM519 TOPICS IN PEOPLE-CENTRED AI Compulsory 15 1
COMM061 NATURAL LANGUAGE PROCESSING Optional 15 2
LAWM165 LAW, ARTIFICIAL INTELLIGENCE AND TECHNOLOGY Optional 15 2

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

Other information

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. In Year 4 of the programme further advanced modules will improve the modelling skills.

The MSc programme learning outcome most closely aligned to the Digital Capability Pillar is
LO3: Select and apply appropriate computational and analytical techniques to model complex problems recognising the limitations of the techniques employed (KCT).

In addition, the following programme LOs partly address the Digital Capability Pillar
LO1: Apply knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems in the subject speciality (K).
LO2: Analyse complex problems to reach substantiated conclusions using first principles of mathematics, statistics, natural science and engineering principles in the subject speciality (KC).
LO5: Design solutions for complex problems that 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 in electronic engineering.(CPT)

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.

The MSc programme learning outcomes most closely aligned to the Employability Pillar are
LO16: Function effectively as an individual, and as a member or leader of a team (PT)
LO17: Communicate effectively on complex engineering matters with technical and non-technical audiences (PT)

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.

The following programme LOs also contributes, in part, to the Global and Cultural Capabilities Pillar
LO5: Design solutions for complex problems that 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 in electronic engineering. (CPT)

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.
The MSc programme learning outcome most closely aligned to the Resourcefulness & Resilience pillar is
LO4: Select and critically evaluate technical literature and other sources of information to solve complex problems (CT)

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.

The MSc programme learning outcome most closely aligned to the Sustainability pillar is
LO7: Evaluate the environmental and societal impact of solutions to broadly-defined problems (CPT)

In addition, the following programme LOs also contributes, in part, to the Sustainability Pillar
LO5: Design solutions for complex problems that 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 in electronic engineering. (CPT)

Quality assurance

The Regulations and Codes of Practice for taught programmes can be found at:

https://www.surrey.ac.uk/quality-enhancement-standards

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.