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.

1x1hr lecture and 1x1hr computer lab per week over 10 weeks as scheduled in MyCampus

None

INFOST4018 AI for the Arts and Humanities (A)

Portfolio of presented lab work 50%

Literature review of a relevant area of AI, word count 3000 (50%)

This course aims to:

■ Engage students critically with current and emerging developments in AI and associated applications, with emphasis on their implications for humanities scholarship

■ Enhance students' advanced practical skills in writing, presenting and critically evaluating code for handling data, experimenting with machine learning, and rendering content, demonstrating awareness of broader coding communities in the arts and humanities

■ Enable students to critically and theoretically examine social, ethical, archival, epistemological and philosophical concerns specific to AI applications in cultural contexts, with engagement with current scholarly debates

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

1. Critically synthesise key developments in AI history and evaluate their disciplinary significance within arts and humanities contexts, demonstrating critical awareness of current issues and forefront developments

2. Critically analyse the general principles of machine learning and its relationship to data, with detailed understanding of epistemological and methodological implications for humanities research

3. Critically evaluate and compare a range of AI models relevant to state-of-the-art approaches, demonstrating critical understanding of specialised theories, concepts and technical principles

4. Demonstrate and critically contextualise advanced skills relevant to machine learning implementation through a sophisticated portfolio of presented code, showing originality and creativity in approach and critical awareness of audience, purpose and scholarly standards

5. Critically analyse and theoretically frame the social, ethical and epistemological implications of AI in focused application areas, making informed judgements on complex issues and engaging substantively with current scholarly debates and emerging concerns