Computer Vision, Robotics and Machine Learning MSc - 2024/5

Awarding body

University of Surrey

Teaching institute

University of Surrey

Framework

FHEQ Levels 6 and 7

Final award and programme/pathway title

MSc Computer Vision, Robotics and Machine Learning

Subsidiary award(s)

Award Title
PGCert Electronic Engineering
PGDip Computer Vision, Robotics and Machine Learning

Professional recognition

Institution of Engineering and Technology (IET)
Accredited by the Institution of Engineering and Technology (IET) on behalf of the Engineering Council for the purposes of fully meeting the academic requirement for registration as an Incorporated Engineer and partially meeting the academic requirement for registration as a Chartered Engineer.

Modes of study

Route code Credits and ECTS Credits
Full-time PFA61033 180 credits and 90 ECTS credits
Part-time PFA61034 180 credits and 90 ECTS credits

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

COLLOMOSSE John (CS & EE)

Date of production/revision of spec

23/07/2024

Educational aims of the programme

Programme learning outcomes

Attributes Developed Awards Ref.

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)
*some programmes may contain up to 30 credits at FHEQ level 6.

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)
*some programmes may contain up to 30 credits at FHEQ level 6.

Programme Adjustments (if applicable)

N/A

Modules

Year 1 (full-time) - FHEQ Levels 6 and 7

Module Selection for Year 1 (full-time) - FHEQ Levels 6 and 7

Three optional modules in Semester 1 to be selected.
No more than one module at FHEQ level 6 from EEE3008 OR EEE3032 may be selected.
One optional module in Semester 2
The 60 credit dissertation module EEEM004 is core.

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

Module Selection for Unstructured (3-5 years) - FHEQ Levels 6 and 7

Four optional modules to be selected from all optional modules, available in Semester 1 and 2.
No more than one module at FHEQ level 6 from EEE3008 or EEE3032 may be selected.
The 60 credit dissertation module EEEM004 is core.

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 2024/5 academic year.