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Programme Title Computational Intelligence and Robotics
Programme Code
JACS Code
ACST17
4 Level of Study
5a Final Qualification
Not applicable
Postgraduate
Master of Science (MSc)
5b
6a
QAA FHEQ Level
Intermediate Qualification(s)
7
Postgraduate Diploma (PGDip), Postgraduate Certificate
(PGCert)
6b
7
QAA FHEQ Level 7
Teaching Institution (if not Sheffield) Not applicable
8 Faculty Engineering
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Department
Other Departments involved in teaching the programme
Automatic Control & Systems Engineering
Computer Science; Psychology
11 Mode(s) of Attendance
12 Duration of the Programme
Full-time
1 year
13
Accrediting Professional or Statutory
Body
14 Date of production/revision
Not applicable
December 2015
15. Background to the programme and subject area
Computational intelligence is an exciting area that brings together research in engineering, computing and neuroscience. Recent advances in how the brain works and applications such as biologically inspired robotics have occurred, in large part, due to a greater understanding of computational intelligence as a unified discipline. You will undertake a broad spectrum of modules related to computational intelligence including vision, speech, neural networks, robotics and computational neuroscience.
You will be taught by world-leading experts from the departments of Automatic Control & Systems Engineering,
Computer Science, and the neuroscience group in Psychology. The teaching will include lectures, seminars, tutorials, individual assignments and a major research project.
This course provides the multi-disciplinary knowledge and skills you will need to meet the demand for experts in computational intelligence.
More information is available at http://www.shef.ac.uk/acse
16. Programme aims
The aims of the programme are to:
1. Provide access to a research biased Masters level degree course in computational intelligence suitable for graduates from a range of backgrounds and disciplines.
2. Provide students with in-depth knowledge and understanding of the rapidly developing field of computational intelligence.
3. Develop in students an independence of thought, an intellectual curiosity and a critical approach to evidence, theories and concepts in a research led environment.
4. Provide teaching that is underpinned and invigorated by the research interests of the staff. acst17 – ver16-17
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5. Improve and augment the existing transferable skills of students, preparing them for a professional research career in computational intelligence and related areas.
In addition for the MSc:
6. Give students the opportunity to undertake a significant research-led project in a particular aspect of computational intelligence.
17. Programme learning outcomes
Knowledge and understanding:
On successful completion of the programme, students for the MSc, PGDip and PGCert will have the knowledge and understanding of:
K1 The general principles of computational intelligence.
K2 Analytical methods relevant to computational intelligence.
K3 Research methods and scientific techniques relevant to computational intelligence.
In addition, students for the MSc and PGDip should have:
K4 The state-of-the-art in computational intelligence including recent developments and the future of the field.
K5 Advanced analytical methods relevant to computational intelligence.
In addition, students for the MSc should have:
K6 The specialist area of their individual research project.
Skills and other attributes:
On successful completion of the programme, students for the MSc, PGDip and PGCert will be able to:
S1 Gather, organise and critically evaluate information needed to formulate and solve problems.
S2 Apply acquired knowledge effectively and efficiently in computational intelligence and related areas.
S3 Display creativity and innovation in solving unfamiliar problems.
S4 Be resourceful, think analytically and construct and sustain logical argument in both oral and written forms.
S5 Carry out individual directed and self-directed study, and participate effectively in group activities.
In addition, students for the MSc will be able to:
S6 Plan and execute a major research based project.
S7 Communicate effectively in both oral and written forms the outcomes of the individual research project.
18. Teaching, learning and assessment
Development of the learning outcomes is promoted through the following teaching and learning methods:

Lectures – used to transmit information, explain theories and concepts and illustrate methods of analysis and design. For many lecture programmes, tutorial/problem sheets are provided to enable students to develop their understanding of the lecture material during private study.
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Coursework assignments – generally require students to seek additional information and work on their own in order to develop greater understanding of, and familiarity with, subject matter.
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Tutorials and example classes – run for the whole class to help students with their understanding and to resolve problems as they work through tutorial/problem sheets.
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Dissertation – a major research study carried out over the summer period. It is supervised by a member of the academic staff but permits the student ample scope to display initiative, originality and creativity. acst17 – ver16-17
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Opportunities to demonstrate achievement of the learning outcomes are provided through the following assessment methods:
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Written examinations – mostly unseen examinations of 2 hours duration.
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Coursework assignments – these include simulation studies, design exercises, computational assignments and practical experiment reporting.
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Oral presentations – all students who submit a project dissertation must also make a short oral presentation of their work to an audience made up academic staff and fellow students.
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Individual project dissertation – a major formal report describing the work undertaken during the
Individual Project phase of the programme.
The main teaching, learning and assessment methods adopted for each learning outcome are shown below. In most cases a combination of methods is used. In the Autumn and Spring Semesters, lectures are the principal means of imparting knowledge, and understanding is gained through a combination of tutorials, practical classes and coursework assignments. Knowledge and understanding are primarily assessed by means of written examinations and programme work. Skills are acquired mainly through coursework and project work.
Students develop research skills through work on an individual project conducted over the summer which then culminates in the writing of a dissertation.
TEACHING /
LEARNING
ASSESSMENT
LEARNING OUTCOME
(abbreviated - see Section 17
for full text)
K1 General principles
K2 Analytical methods
K3 Research methods
K4 State-of-the-art
K5 Advanced analytical methods
K6 Specialist knowledge
S1 Evaluate information
S2 Apply knowledge
S3 Creativity and innovation
S4 Logical argument
S5 Individual and group work
S6 Research project
S7 Communication
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19. Reference points
The learning outcomes have been developed to reflect the following points of reference:
The University Learning, Teaching and Assessment Strategy.
The Departmental Learning, Teaching and Assessment Strategies of the Departments of Automatic Control &
Systems Engineering, Computer Science and Psychology.
The research interests of the academic staff and the research strategies of the Departments of Automatic
Control & Systems Engineering, Computer Science and Psychology.
The appropriate qualification descriptors contained in the QAA Framework for Higher Education Qualifications in England, Wales and Northern Ireland – August 2008.
20. Programme structure and regulations
The programme runs full time for 12 months. Students will undertake 60 credits of taught modules in the
Autumn semester and 60 credits of taught modules in the Spring semester. Students will also undertake a substantial individual research programme worth a further 60 credits during the summer. The MSc degree is awarded upon accumulation of 180 credits. For students achieving only 60 credits and 120 credits they can be awarded the PGCert and PGDip respectively.
Detailed information about the structure of programmes, regulations concerning assessment and progression and descriptions of individual modules are published in the University Calendar available on-line at http://www.shef.ac.uk/govern/calendar/regs.html
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21. Student development over the course of study
Semester 1 – Students will undertake 50 credits of taught modules covering basic computational neuroscience and topics in computational intelligence. In addition students will undertake a 10 credit module on research skills that will provide training in generic research skills and introductory material to support other modules.
Semester 2 – Students will undertake 60 credits of taught modules that build on the semester 1 modules.
These will include advanced computational neuroscience and additional topics in computational intelligence.
By the end of semester 2, students will have a broad knowledge of subjects in computational intelligence and the necessary specialist knowledge and research skills to undertake a substantial research project.
Summer – Students will undertake a substantial individual research project under the supervision of a member of academic staff. On successful completion of the research project, students will have further developed their research and general project management skills and will be able to demonstrate initiative, independence of thought and critical appraisal of research.
22. Criteria for admission to the programme
Minimum 2.1 honours degree or equivalent in a numerate subject such as engineering, mathematics or physical sciences. Students with alternative backgrounds will be considered on an individual basis. General
University requirements regarding English qualifications must also be satisfied.
23. Additional information
For further information, students are directed to the Department of Automatic Control & Systems Engineering website at http://www.shef.ac.uk/acse
Further information is also available at the Departments of Computer Science and Psychology websites:
Computer Science – http://www.shef.ac.uk/dcs/
Psychology – http://www.shef.ac.uk/psychology/
This specification represents a concise statement about the main features of the programme and should be considered alongside other sources of information provided by the teaching department(s) and the University.
In addition to programme specific information, further information about studying at The University of Sheffield can be accessed via our Student Services web site at http://www.shef.ac.uk/ssid . acst17 – ver16-17
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