Course Catalogue

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Artificial Intelligence (AI) is increasingly featured in all areas of working with digital information, whether it is creation, interpretation, communication, and/or use. This places it at the centre of the social and cultural impact of the digital revolution. This course goes beyond philosophy and ethics to help you gain the practical skills valuable for a deeper understanding of the basic mechanics of AI and their applications. The course intends to empower a broader audience, with a focus in the arts and humanities, to engage in a deeper discussion about current debates regarding AI, across technical, social and cultural dimensions. It prepares students with the foundation to undertake the course AI for the Arts and Humanities (B) where they will explore how AI can augment human creative processes.

This will be a blended course. 1x1hr per week lecture and 1x1hr per week computer lab over 10 weeks as scheduled on MyCampus. Lecture will be delivered online either live and/or pre-recorded. Supervised computer labs will be in-person by default.

Available to all students fulfilling requirements for Honours entry into Digital Media and Information Studies, and by arrangement to visiting students or students of other Honours programmes who qualify under the University's 25% regulation.

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The assessment will comprise a portfolio (50%) and a report (50%).

The portfolio (2500 words) will comprise presented code contextualised with written text, image, and/or sound.

The report (1500 words) will consist of a review of a selected area/aspects of AI.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The course aims to:

■ Engage students with a range of current developments in AI and associated applications.

■ Enhance students' practical skills in writing and presenting code (e.g. for handling data, experimenting with machine learning, and rendering content) to engage with the broader coding community in the arts and humanities.

■ Help students critically examine social concerns (e.g. ethical, archival, philosophical) specific to AI-applications.

By the end of this course student will be able to:

■ Formulate a overview of key developments in AI history and their disciplinary significance.

■ Distinguish the general principles of machine learning and its relationship to data.

■ Contrast a selection of AI models relevant to current state-of-the-art approaches (e.g. different types of neural networks).

■ Illustrate skills relevant to the steps of machine learning in a portfolio of presented code (computer code accompanied by written text, comments and relevant images and/or audio to engage a broader audience).

■ Review critically the social implications of AI in a focused application area.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.