Machine Learning, Mathematics and Statistics, BSc (Hons)

This degree programme aims to equip students with a rigorous understanding of the mathematical and statistical foundations of Machine Learning and Artificial Intelligence, including linear algebra, calculus, probability theory, and statistical inference. It fosters critical analysis and construction of theoretically grounded algorithms and models, considering computational complexity and statistical validity, while providing insights into Machine Learning and Deep Learning paradigms to drive algorithmic innovation and emerging applications. The programme instils passion for algorithms and complexity to ensure efficient solutions, cultivates awareness of ethics, transparency, accountability, and regulatory issues—along with advocacy skills—and explores the history and philosophy of these fields to appreciate their scope and limitations. It prepares graduates for responsible roles in the IT industry, promotes independent study, elective topics for research readiness, and enhances transferable skills like communication and teamwork. Additionally, it emphasizes sustainable computing through knowledge, values, and actions for systemic societal solutions, while building research skills to evaluate literature, plan investigations, and contribute to the state of the art in chosen problem spaces.Aligned with recommendations from the UK's QAA and the US's Association for Computing Machinery (ACM), the programme acknowledges the vast growth in Computing Science knowledge, making comprehensive coverage impossible. Instead, QAA Benchmarks and ACM Body of Knowledge outline core graduate attributes and essential knowledge, allowing institutions to define specialisms for deep study in targeted areas. These specialisms leverage the School of Computing Science's strengths and are shaped through consultations with industry partners.