The Michaelmas term of Social Data Science introduces students to the mathematical and statistical foundations of Machine Learning. This course extends this learning with some recent advances in AI focusing on language models and related applications.
Topics covered include transformers, fine-tuning, human-AI interaction and evaluation strategies. Applications will be align with the expertise and research areas of the faculty.
Learning outcomes include understanding key traditional and generative AI approaches; critically assessing these methods (e.g. their internal workings versus claims made about their performance); and engaging with the social context surrounding these methodologies and their applications.
