MSc Artificial Intelligence
01 Sep 2022 - 31 Aug 2028
| Course Leader | Mabrouka Abuhmida |
|---|---|
| Course Team | Ian Wilson, Andrew Ware, Carl Jones, Rebecca Peters |
| Awarding Body | University of South Wales |
| Teaching Institutions | University of South Wales |
| Modes of Study | Full Time, Part Time |
Document Version
| Version | 2 |
|---|---|
| Valid From | 01 Sep 2022 |
| Valid To | 31 Aug 2028 |
QAA Benchmarks
Educational Aim
To develop an ability to reformulate and use practical, conceptual, and technological understanding of machine intelligence to create ways forward in contexts where there are many interacting factors.
To enhance the student’s ability to critically analyse, interpret and evaluate complex information, concepts and theories that is informed by research at the forefront of developments in machine intelligence.
To enable students to understand the wider contexts in which intelligent computer systems are found including an awareness of relevant legal, social, ethical and professional issues.
To develop an ability to undertake design and development activities within the field of machine intelligence based on a systematic understanding of essential principles and practices, tools and techniques.
To enable students to critically evaluate their own actions, methods and results, as well as those of others, and their short and long-term implications.
To develop an attitude of personal responsibility and entrepreneurship when planning and developing courses of action, which includes recognising and responding to opportunities for innovation.
To allow demonstration of an advanced ability to develop industry-standard software, along with supporting design and test documentation, entailing development in an industry-appropriate language, presentation and manipulation of objects, and creativity or innovation derived from the student's own interests.
To develop depth of knowledge and understanding of recurring themes such as abstraction, complexity and evolutionary change that will facilitate the development of lifelong learners in a rapidly changing profession.
To develop a life-long learner, who can exercise broad autonomy and judgement, set goals and identify resources.
To further develop transferable skills, including communication, working with others, problem solving and improving own learning and performance, and apply these to non-trivial problems.
Learning Outcomes
| A1 | To identify and describe appropriate theoretical and practical approaches to solving problems utilising machine intelligence. |
| A2 | To show discernment in the synthesis of principles and practices governing the development of intelligent computer systems to solve real-world problems. |
| A3 | To explain the wider contexts, including legal, social, ethical and professional matters, within which the field of machine intelligence operates. |
| A4 | To critically review complex information, concepts and theories at the forefront of the discipline to inform decision making. |
| A5 | To critically describe practical, conceptual or technological knowledge necessary to create solutions to problems in machine intelligence contexts. |
| A6 | To demonstrate knowledge of applicable research techniques. |
| B1 | To further develop investigative, research, writing and presentation skills as a self-directed, autonomous learner. This can be measured in the MSc Project module. |
| B2 | To initiate and lead complex tasks and processes, taking responsibility – where relevant – for the work and roles of others. |
| B3 | To take responsibility for planning and developing a project, and to exercise broad autonomy and judgement across a significant area of work or study. |
| B4 | To be able to critically analyse, design, implement and evaluate systems and solutions that embody artificial intelligence where there are many interacting factors. |
| B5 | To exercise autonomy and judgement in the selection of optimal solutions to problems. |
| B6 | To plan and develop courses of action that initiate or underpin substantial developments utilising machine intelligence. |
| C1 | To determine and use appropriate project management techniques, research methods and tools within an appropriate ethical framework. |
| C2 | To conceptualise a problem situation that involves many interacting factors, and to determine and use appropriate approaches to producing a software solution. |
| C3 | To determine appropriate methods for interrogating large datasets in order to extract information that can be used to inform decision making processes to meet business goals. |
| C4 | To determine and use appropriate design and modelling techniques to develop intelligent computer system applications within legislative constraints. |
| C5 | To be able to apply strategic, practical and conceptual understanding in the broad context of intelligent computer system development. |
| C6 | To produce a justified solution to a significant problem that is informed by a critical review of research. |
Course Structure
Level 7 Modules
| Module Code | Module Id | Module Title | Module Status | Credit Value | Module Type |
|---|---|---|---|---|---|
| CS4S770 | MOD011109 | Knowledge-Based Systems | Running | 20 | specified |
| CS4S771 | MOD011110 | Machine Learning and Autonomous Systems | Running | 20 | specified |
| CS4S772 | MOD011111 | Deep Learning | Running | 20 | specified |
| CS4S773 | MOD012615 | Computational Applications of Artificial Intelligence | Running | 20 | specified |
| CS4T702 | MOD012616 | MSc Project | Running | 60 | core |
| IS4S706 | MOD000995 | Project Management and Research Methodology | Running | 20 | specified |
| IS4S761 | MOD009823 | Principles of Computing | Running | 20 | specified |
Teaching and Assessment
Learning and Teaching Methods
Employer Engagement
Visiting Speakers - Staff will utilize industry contacts primarily associated with research projects (e.g. KESS and KTP) to provide visiting speaker opportunities when and where required.
Empolyer Forums - Staff have strong informal relationships with employers developed over 20 years. This course will provide a focus for a local advisory board. These forums have and will continue to inform course development.
Other - Most modules use case studies, scenarios, and examples from the cyber security industry to illustrate concepts and their importance. Opportunities for work-related learning activities continue as students engage in and contribute in a positive manner to the solution of world of work tasks and problems.
Means of Assessment
Formative Assessment - Lectures and tutorials contain formative exercises to encourage students to experiment and gain practical experience.
Learning Support
Induction
The School plans and runs a programme of induction activities during the first week of attendance for both new and returning students.
Induction lectures are recorded and made available to students through Blackboard, supporting students who join the course after induction week or do not attend. These events introduce students to the course, basic USW and IT regulations. Students get to meet the course staff and learn about basic online and physical facilities.
Personal Academic Coach
The Course Leader acts as personal tutor who is able to meet students on a regular basis. The typically small cohort size means that the Course Leader quickly recognises each student and identifies each one’s engagement and progress. If the cohort size was to significantly increase then a Course Tutor would be appointed to assist the Course Leader.
Office hours
Normal university office hours apply, which is typically 08:30 to 17:00.
Students are informed of the open-door policy and are always welcome to chat with the course team when they are available on campus.
In addition, most team members publish hours when they are available for drop-in sessions without appointment. Staff is also available on emails and teams.
Tutorials
Every taught module has tutorial and/or practical hours associated with every hour of lecture, where students are able to practice what they are learning and receive individual support.
Seminars
Tutor-supported seminars allow flexible classroom time for students to learn by doing, to practice, to discuss and to demonstrate their work.
Progress meetings
Each student will meet their personal tutor once a term to discuss progress.
Research Supervision
A student would meet their project supervisor a minimum of three times, typically towards the start and end of the project life-cycle. For a part-time student equivalent support will be provided, however this will need to be adapted to fit the timing of their project.
Online Resources
Teaching and coursework assessment materials are made available on-line through the University’s virtual learning environment (VLE).
Modern computing laboratories provide access to specialist resources. The University also has centrally-managed open-access laboratories for more general work. Each student has an academic e-mail account that is particularly useful when requesting support from teaching and tutorial staff.
Some modules on the course also schedule additional online drop-in sessions to provide students more flexibility who might have missed on campus sessions.
Advice Zone
The University operates an Advice Zone located in the Library.
DDS Service
The University runs a DDS Service that can agree an Individual Support Plan. The Plan summarises the support that has been agreed.
IT/Library
The University has a modern library that provides access to textbooks, journals, on-line materials and equipment. There are open-access computer laboratories in the Library.
The School maintains five computer laboratories available to students based in the Computer Science department that are equipped with 130 Linux, Windows and MacOS workstation, which supplement the university maintained general access laboratories. In addition, students will have access to the Artificial Intelligence and Robotics workshop, which itself has limited access.
Course Exit Points
| Award | Criteria | Final |
|---|---|---|
| Master of Science | 180 credits of which at least 150 must be at Level 7 and no more than 30 at Level 6 | Final |
| Postgraduate Diploma | 120 credits of which at least 90 must be at Level 7 and no more than 30 at Level 6 | Exit |
| Postgraduate Certificate | 60 credits with at least 40 at Level 7 and no more than 20 at Level 6 | Exit |
Progression Route
Typically MSc graduates continue their careers in industry or commence doctoral research.
Entry Requirements
Admission to the course is typically through the following qualifications:
The School of Computing and Mathematics seeks actively to promote University policies on equal opportunities and widening access and will seek to recruit as wide a range of students as the current mode of attendance and admission requirements permit.
The procedures, criteria and regulations for admission, including promotion of wider access and equal opportunities will follow those established for the existing post-graduate provision offered by the School of Computing and Mathematics. Normally, evidence will be sought of successful completion of an under-graduate Honours degree and, where appropriate, a minimum average IELTS (International English Language Testing System) score of 6.5.
This course is aimed at graduates with a minimum 2:2 Honours degree or equivalent who would like to broaden their existing knowledge and open up a new career path. Applications from engineering, IT, science, mathematics or business graduates in particular are welcomed. All entrants must have strong numeracy and IT skills.
Candidates applying to the course with non-standard qualifications will be judged on an individual basis using Recognition of Prior Learning procedures as defined in the University’s Regulations. For example, the University may admit students on the basis of their prior experiential learning, provided that it is identifiable, relevant to the programme of study for which they are applying and provides sufficient evidence of their ability.
Inclusive Curriculum Statement
The University of South Wales operates a policy of inclusive learning, teaching and assessment to ensure that all students have an equal opportunity to fulfil their educational potential. Course teams will have considered ways of designing out any potentially disadvantageous element of courses during the course design process. However some specific needs may remain, details about how to apply to have your needs assessed can be found at: http://unilife.southwales.ac.uk/pages/3040-disability-and-dyslexia-service/
Addendum for Delivery at a Partner Institution
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Methods Of Quality Standards
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Quality Of Standards Indicators
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