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Teaching

The MSc Artificial Intelligence course is designed to give you a professional understanding of AI theory and practice focussed on the practical application of AI through the design, build and testing of AI systems. Although AI is a technical subject, our course will ensure you don’t lose sight of other crucial professional aspects, such as ethical responsibility, entrepreneurship and sustainability.

The course will equip you for a professional mastery of AI theory and practice.

The aims of the course are:

• Provide an intellectually challenging and vocationally relevant learning experience that incorporate the latest research and contemporary ideas in Artificial Intelligence.
• Progress students through structured learning that will give you an accessible introduction to AI theory and practice and build on these to make you a confident AI practitioner.
• Provide a high-quality and highly relevant learning environment with teaching staff from a wealth of backgrounds including industrial and business applications, and advanced research establishments.
• Create practical experience in exploring the latest technological and computing technologies, such as AI programming, machine learning and optimisation.

You will be taught through a combination of interactive workshops, lectures, seminars and laboratory practicals, fieldwork, practical activities etc. Interactive workshops take a variety of formats and are intended to enable the application of learning through discussion and small group activities. Seminars enable the discussion and development of understanding of topics covered in lectures, and laboratory practical’s are focused on developing subject specific skills and applied individual and group project work.

In addition, meetings with Personal Academic Tutors (PAT) are scheduled during your studies. Meetings are also scheduled with the Research Project supervisors throughout the execution of the project.

Your Personal Academic Tutor (PAT) will normally be a subject specialist and will advise you of when they are available and how they prefer to be contacted. You should see your Personal Academic Tutor as your first point of contact to discuss any wider issues or problems that arise during your time at University. Your Tutor will listen, offer initial advice and, if necessary, direct you to other appropriate sources of help, including the broad range of services available within the University.

You will have access to a range of dedicated teaching resources including dedicated computer labs and study areas.

Contact time

In a typical week there will be at least 12 hours of timetabled teaching in lectures, seminars and small-group work. The precise contact hours will depend on the semester of study. For part-time students, this will depend on the number of modules being taken. Typically, class contact time will be structured around:

• Delivery of theoretical content to address contemporary challenges in Artificial Intelligence.
• Practical in-class tasks relating theory to practice.
• Discussion and group activities.

Independent self-study

In addition to the contact time, you are expected to undertake around 30 hours of personal self-study per week. Typically, this will involve completing online activities, reading journal articles and books, working on individual and group projects, undertaking research in the library and online, preparing coursework assignments and presentations, and preparing for examinations.

Independent learning is supported by a range of excellent learning facilities, including The Hive and library resources, the virtual learning environment, and extensive electronic learning resources.

Duration

• 1 Year full-time 
• 2 Years at least for part-time (maximum registration 6 years)

Timetables

Timetables are normally available one month before registration.

Please note that whilst we try to be as student-friendly as possible, scheduled teaching can take place on any day of the week; and some classes can be scheduled in the evenings.

Assessment

The course provides opportunities to test understanding and learning informally through the completion of practice or ‘formative’ assignments.

Each module has one or more formal or ‘summative’ assessments which are graded and count towards the overall module grade. Assessment methods include written examinations and a range of coursework assessments such as essays, reports, portfolios, performance, presentations and a final year independent studies project.

Programme specification

For comprehensive details on the aims and intended learning outcomes of the course, and the means by which these are achieved through learning, teaching and assessment,