Artificial intelligence

This applied, tools-oriented course introduces Artificial Intelligence (AI) as a broad field, positioning contemporary methods within their historical and societal context while emphasising practical understanding over formal mathematics. Students examine AI as systems that optimise decisions from data, progressing from foundational concepts and data analytics to machine learning (supervised/unsupervised/reinforcement), deep learning, and large language models (LLMs). The course highlights the capabilities of generative AI, AI agents, and automation, as well as an end-to-end workflow from data collection to deployment, utilising Python/MATLAB/cloud, and no-code platforms. Ethical, legal and societal implications – privacy, bias, transparency, safety, and regulation – are integrated throughout. By the end, participants can recognise appropriate AI use cases, interpret model behaviour and limitations, and select suitable tools to prototype AI solutions responsibly.