Computer Vision, Robotics and Machine Learning - 2017/8

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

Subsidary award(s)

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

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

JACs code

I440, I440

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

COLLOMOSSE JP Dr (Elec Elec En)

Date of production/revision of spec

22/09/2017

Educational aims of the programme

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).

Attract well-qualified entrants, with a background in Electronic Engineering, Physical Sciences, Mathematics, Computing & Communications, from the UK, Europe and overseas.

Provide participants with advanced knowledge, practical skills and understanding applicable to the MSc degree.

Develop participants' understanding of the underlying science, engineering, and technology, and enhance their ability to relate this to industrial practice.

Develop participants' critical and analytical powers so that they can effectively plan and execute individual research/design/development projects.

Provide a high level of flexibility in programme pattern and exit point.

Provide students with an extensive choice of taught modules, in subjects for which the Department has an international and UK research reputation.

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 that underpin computer vision, machine learning as well as how they can be related to robotics.

Engineering problem solving - A graduate from this MSc Programme should be able to analyse problems within the field computer vision 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.

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 within computer vision, machine learning.

Societal and environmental context - A graduate from this MSc Programme should be aware of the societal and environmental context of his/her engineering activities.

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

Research & development investigations - A graduate from this MSc Programme should be able to carry out research-and-development investigations.

Design - A graduate from this MSc Programme should where relevant, be able to design electronic circuits and electronic/software products and systems.

This programme in Computer Vision, Robotics and Machine Learning aims to provide a high-quality advanced training in aspects of computer vision for extracting information from image and video content or enhancing its visual quality using machine learning codes. Computer vision technology uses sophisticated signal processing and data analysis methods to support access to visual information, whether it is for business, security, personal use or entertainment. The core modules cover the fundamentals of how to represent image and video information digitally, including processing, filtering and feature extraction techniques. An important aspect of the programme is the software implementation of such processes. Students will be able to tailor their learning experience through selection of elective modules to suit their career aspirations. Key to the programme is cross-linking between core methods and systems for image and video analysis applications. The programme has strong links to current research in the Department of Electronic Engineering's Centre for Vision, Speech and Signal Processing.

Programme learning outcomes

Attributes Developed
Describe some of the theories and ideas of computer vision, robotic operation and machine based learning
Describe the fundamental operations that can be applied to the signal and data as well as the information that can be extracted by using a range of sophisticated computer vision and machine learning methods,
Describe and compare the characteristics of methods used in computer vision, robots and machine learning
Demonstrate transferable skills such as problem solving, analysis and critical interpretation of data
Describe some of the theories and ideas of computer vision, robotic operation and machine based learning
Describe the fundamental operations that can be applied to the signal and data as well as the information that can be extracted by using a range of sophisticated computer vision and machine learning methods
Demonstrate transferable skills such as problem solving, analysis and interpretation of data.
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 & 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
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
Underpinning engineering. Be able to apply and integrate knowledge and understanding of other engineering disciplines to support study of electronic and electrical engineering. C
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
Engineering principles and analysis. Understand electronic and electrical engineering principles and be able to apply them to analyse key engineering processes. KCP
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
Use of quantitative methods for problem solving. Be able to apply quantitative methods relevant to electronic and electrical engineering, in order to solve engineering problems. C
Systems thinking. Understand and be able to apply a systems approach to electronic and electrical engineering problems. KCP
Workshop & laboratory skills. Have relevant workshop and laboratory skills. P
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
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
Current and future practice. Have thorough understanding of current practice and limitations, and some appreciation of likely future developments. K
Emerging technologies. Be aware of developing technologies related to electronic and electrical engineering. K
Deepened knowledge of underlying scientific principles. Have comprehensive understanding of the scientific principles of electronic engineering and related disciplines. KC
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
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
Deepened knowledge of materials and components. Have extensive knowledge of a wide range of engineering materials and components. K
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
Sustainable development. Understand the requirement for engineering activities to promote sustainable development. K
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
Ethical conduct. Understand the need for a high level of professional and ethical conduct in engineering. K
Commercial context. Know and understand the commercial and economic context of electronic and electrical engineering processes. K
Engineering applications. Understand the contexts in which engineering knowledge can be applied (e.g. operations and management, technology development, etc.) K
Intellectual property. Be aware of the nature of intellectual property. K
Codes of practice. Understand appropriate codes of practice and industry standards. K
Quality. Be aware of quality issues. K
Working under constraints. Be able to apply engineering techniques taking account of a range of commercial and industrial constraints. CT
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
Regulation. Be aware of the framework of relevant legal requirements governing engineering activities, including personnel, health, safety and risk (including environmental risk) issues. K
Technical information. Understand the use of technical literature and other information sources. T
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
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
Technical uncertainty. Be able to work with technical uncertainty. CT
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
Customer needs. Understand customer and user needs and the importance of considerations such as aesthetics. KT
Cost drivers. Identify and manage cost drivers. CT
Creativity. Use creativity to establish innovative solutions. CPT
Design-life issues. Ensure fitness for purpose and all aspects of the problem including production, operation, maintenance and disposal. KC
Design management. Manage the design process and evaluate outcomes CT
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
Innovative design. Be able to generate an innovative design for products, systems, components or processes, to fulfil new needs. CP
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
Business practice. Have extensive knowledge and understanding of management and business practices, and their limitations, and how these may be applied appropriately. K

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 two academic years, 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.

Programme Adjustments (if applicable)

N/A

Modules

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 2017/8 academic year.