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
20/12/2024
Educational aims of the programme
- The overarching aim of the MSc programme in Computer Vision, Robotics and Machine Learning MSc is to provide a high-quality advanced training in aspects of computer and robotic vision for extracting information from image and video content and enhancing its visual quality using machine learning codes. Core modules cover the fundamentals of how to represent image and video information digitally, including processing, filtering and feature extraction techniques. Students will be able to tailor their learning experience through selection of elective modules to suit their interests and career aspirations. Key to the programme is cross-linking between core methods and systems for image and video analysis applications. 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 ensure that our MSc programmes completely satisfy the educational requirements for Chartered Engineer status thereby allowing our graduates to obtain professional recognition.
- 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 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 concepts and tools in analysis of engineering problems.
- 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 demonstrate their knowledge, understanding and application of mathematical, scientific, and engineering principles.
- 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 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 knowledge and awareness of sustainability via consideration of sustainability issues such as the UN's Sustainability Development Goals appropriate to their programme.
- 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.
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 computer vision, robotics and machine learning. | KC | PGDip, MSc | M1 |
| Formulate and analyse complex problems in computer vision, robotics and machine learning to reach substantiated conclusions. | K | PGDip, MSc | M2 |
| Select and apply appropriate computational and analytical techniques to model complex problems in computer vision, robotics and machine learning, discussing the limitations of the techniques employed | KCT | PGDip, MSc | M3 |
| Select and critically evaluate technical literature and other sources of information to solve complex problems in computer vision, robotics and machine learning | CT | PGDip, MSc | M4 |
| Design solutions for complex problems in computer vision, robotics and machine learning 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, MSc | M5 |
| Evaluate the environmental and societal impact of solutions to complex problems in computer vision, robotics and machine learning (to include the entire life-cycle of a product or process) and minimise adverse impacts | CP | PGDip, MSc | M7 |
| Function effectively as an individual, and as a member or leader of a team. Evaluate effectiveness of own and team performance | PT | 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 knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems in electronic engineering. | KC | MSc | |
| Formulate and analyse problems in electronic engineering to reach substantiated conclusions. | KC | MSc | |
| Select and apply appropriate computational and analytical techniques to model problems in electronic engineering | KCT | MSc | |
| Select and evaluate technical literature and other sources of information to solve problems in electronic engineering | CT | MSc | |
| 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 | MSc |
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:
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.