Artificial Intelligence MSc - 2020/1

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 Artificial Intelligence

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

100159

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”; 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 - Electrical and Electronic Engineering

Programme Leader

SONG Yi-Zhe (Elec Elec En)

Date of production/revision of spec

25/10/2020

Educational aims of the programme

  • Attract well-qualified entrants, with a background in Electronic Engineering, Computer Science, Physical Sciences, Mathematics, and Communications from the UK, Europe and overseas.
  • Design - A graduate from this MSc Programme should be able to design and test electronic circuits and record sensor data, design software products and systems.
  • Develop participants' critical and analytical powers so that they can effectively plan and execute individual research/design/development projects.
  • Develop participants' understanding of the underlying science, engineering, and technology, and enhance their ability to relate this to industrial practice.
  • 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 engineering activities.
  • 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 electronic engineering and find solutions.
  • Engineering tools - 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 engineering tasks.
  • Provide a high level of flexibility in programme pattern and exit point.
  • Provide participants with advanced knowledge, practical skills and understanding applicable to the MSc degree.
  • Provide students with an extensive choice of taught modules, in subjects for which the Department has an international and UK research reputation.
  • Research + development investigations - A graduate from this MSc Programme should be able to carry out research-and-development investigations.
  • Societal and environmental context - A graduate from this MSc Programme should be aware of the societal and environmental context of his/her engineering activities.
  • Technical expertise - 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.
  • The MSc in Artificial Intelligence (AI) is a comprehensive programme that incorporates topics from almost all areas of AI. Students will gain insight knowledge and solid practical skills across four mainstream AI themes (Vision, Speech, and 5G and Future generation Communications), each of which is underpinned by its respective research group that is world-leading. The programme uniquely blends in the business, regulation and ethics sides of modern AI, which will further prepare students with skill set desired to engage with AI industry and pursue entrepreneurial opportunities. Developing practical machine learning skills will be at the core of this programme, where students will gain experience with cutting-edge tools such as Deep Neural Networks (DNN) and Reinforcement Learning (RL) within the context of different research themes via regular exercises and practical labs. The programme brings together our teaching, research and industrial contacts to allow the cohort to mix the different themes that best suits their personal requirements and future plan. Students will be taught and supervised by world-renowned academics in their specific subject areas, and have regular contacts with them throughout the duration of the programme. The Programme has strong links to current research in the Department of Electronic Engineering's Centre for Vision, Speech and Signal Processing.
  • The taught postgraduate Degree Programmes of the Department are intended both to assist with professional career development within the relevant industry and, for a small number of students, 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 skills (where relevant).
  • Underpinning learning- A graduate from this MSc Programme should know, understand and be able to apply the fundamental mathematical, scientific and engineering facts and principles, including societal principles, that underpin AI

Programme learning outcomes

Attributes Developed Awards Ref.
Describe some of the theories and ideas on which Artificial Intelligence are founded PGDip
Describe the fundamental operation and information that can be obtained from a range of sophisticated Artificial Intelligence tools PGDip
Describe and compare the characteristics of components used in Artificial Intelligence skills such as problem solving, analysis and critical interpretation of data PGDip
Describe of some of the theories and ideas of Artificial Intelligence PGCert
Describe the operation of some tools of Artificial Intelligence PGCert
Demonstrate transferable skills such as problem solving, analysis and interpretation of data PGCert
IT tools. Be able to use computers and basic IT tools effectively. T
Information retrieval. Be able to retrieve information from written and electronic sources. T
Information analysis. Be able to apply critical but constructive thinking to received information. T
Studying. Be able to study and learn effectively. T
Written and oral communication. Be able to communicate effectively in writing and by oral presentations. T
Presenting quantitative data. Be able to present quantitative data effectively, using appropriate methods. T
Time and resource management. Be able to manage own time and resources. T
Planning. Be able to develop, monitor and update a plan, in the light of changing circumstances. T
Personal development planning. Be able to reflect on own learning and performance, and plan its development/improvement, as a foundation for life-long learning. T
Underpinning science. Know and understand scientific principles necessary to underpin their education in electronic and electrical engineering, to enable appreciation of its scientific and engineering content, and to support their understanding of historical, current and future developments. KC US1
Underpinning mathematics. Know and understand the mathematical principles necessary to underpin their education in electronic and electrical engineering and to enable them to apply mathematical methods, tools and notations proficiently in the analysis and solution of engineering problems. KCP US2
Underpinning engineering. Be able to apply and integrate knowledge and understanding of other engineering disciplines to support study of electronic and electrical engineering. C US2
Analysis and modelling of systems and components. Be able to identify, classify and describe the performance of systems and components through the use of analytical methods and modelling techniques. CP E2
Use of mathematical and computer-based models. Be able to apply mathematical and computer-based models to solve problems in electronic and electrical engineering, and be able to assess the limitations of particular cases. CP E2m
Engineering principles and analysis. Understand electronic and electrical engineering principles and be able to apply them to analyse key engineering processes. KCP E1
Use of quantitative methods for problem solving. Be able to apply quantitative methods relevant to solve engineering problems. C E3 (part)
Systems thinking. Understand and be able to apply a systems approach to electronic and electrical engineering problems. KCP E4
Workshop + laboratory skills. Have relevant workshop and laboratory skills. P P2
Programming + software design. Be able to write simple computer programs, be aware of the nature of microprocessor programming, and be aware of the nature of software design CP
Software tools. Be able to apply computer software packages relevant to electronic and electrical engineering, in order to solve engineering problems. CP E3 (part)
Topic-specific knowledge. Know and understand the facts, concepts, conventions, principles, mathematics and applications of the range of electronic and electrical engineering topics he/she has chosen to study. KCP
Characteristics of materials and engineering artefacts. Know the characteristics of particular materials, equipment, processes or products. K P1
Current and future practice. Have thorough understanding of current practice and limitations, and some appreciation of likely future developments. K P1m
Emerging technologies. Be aware of developing technologies related to electronic and electrical engineering. K US2m
Deepened knowledge of underlying scientific principles. Have comprehensive understanding of the scientific principles of electronic engineering and related disciplines. KC US1m
Deepened knowledge of mathematical and computer models. Have comprehensive knowledge and understanding of mathematical and computer models relevant to electronic and electrical engineering, and an appreciation of their limitations. KCP US3m
Deepened topic-specific knowledge. Know and understand, at Master's level, the facts, concepts, conventions, principles, mathematics and applications of a range of engineering topics that he/she has chosen to study. KCP (m)
Deepened knowledge of materials and components. Have extensive knowledge of a wide range of engineering materials and components. K P2m
Broader grasp of relevant concepts. Understand concepts from a range of areas including some from outside engineering, and be able to apply them effectively in engineering projects. KC US4m
Sustainable development. Understand the requirement for engineering activities to promote sustainable development. K S3
Legal requirements relating to environmental risk. Relevant part of: Be aware of the framework of relevant legal requirements governing engineering activities, including personnel, health, safety and risk (including environmental risk issues. K S4 (part)
Ethical conduct. Understand the need for a high level of professional and ethical conduct in engineering. K S5
Commercial context. Know and understand the commercial and economic context of electronic and electrical engineering processes. K S1
Engineering applications. Understand the contexts in which engineering knowledge can be applied (e.g. operations and management, technology development, etc.) K P3
Intellectual property. Be aware of the nature of intellectual property. K P5
Codes of practice. Understand appropriate codes of practice and industry standards. K P6
Quality. Be aware of quality issues. K P7
Working under constraints. Be able to apply engineering techniques taking account of a range of commercial and industrial constraints. CT P3m
Financial Accounting. Understand the basics of financial accounting procedures relevant to engineering project work. K
Commercial risk. Be able to make general evaluations of commercial risks through some understanding of the basis of such risks. CT S2m
Regulation. Be aware of the framework of relevant legal requirements governing engineering activities, including personnel, health, safety and risk (including environmental risk) issues. K S4 (part)
Technical information. Understand the use of technical literature and other information sources. T P4
Need for experimentation. Be aware of the need, in appropriate cases, for experimentation during scientific investigations and during engineering development. K
Investigation of new technology. Be able to use fundamental knowledge to investigate new and emerging technologies. CP E1m
Problem-solving using researched data. Be able to extract data pertinent to an unfamiliar problem, and employ this data in solving the problem, using computer-based engineering tools when appropriate. CP E3m
Technical uncertainty. Be able to work with technical uncertainty. CT P8
Understanding design. Understand the nature of the engineering design process. K
Design specification. Investigate and define a problem and identify constraints, including environmental and sustainability limitations, and health and safety and risk assessment issues. C D1
Customer needs. Understand customer and user needs and the importance of considerations such as aesthetics. KT D2
Cost drivers. Identify and manage cost drivers.D3 CT D3
Creativity. Use creativity to establish innovative solutions. CPT D4
Design-life issues. Ensure fitness for purpose and all aspects of the problem including production, operation, maintenance and disposal. KC D5
Design management. Manage the design process and evaluate outcomes CT D6
Design methodologies. Have wide knowledge and comprehensive understanding of design processes and methodologies and be able to apply and adapt them in unfamiliar situations. KCP D1m
Innovative design. Be able to generate an innovative design for products, systems, components or processes, to fulfil new needs. CP D1m
Team membership. Be able to work as a member of a team. T
Team leadership. Be able to exercise leadership in a team. T
Multidisciplinarity. Be able to work in a multidisciplinary environment. T
Management awareness. Know about management techniques that may be used to achieve engineering objectives within the commercial and economic context of engineering processes. K S2
Business practice. Have extensive knowledge and understanding of management and business practices, and their limitations, and how these may be applied appropriately. K S1m

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 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 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

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
ERASMUS Study (that is not taken during Level P) N
Study exchange(s) (that are not part of the ERASMUS scheme) N
Dual degree N

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 2020/1 academic year.