Course summary

This degree programme aims to: • empower students with a rigorous comprehension of the fundamental Mathematical and Statistical foundations that underpin Machine Learning and Artificial Intelligence, including linear algebra, calculus, probability theory and statistical inference; • equip students with the ability to critical analyse as well as construct algorithms and models that are cemented in theoretical principles and considered from perspectives including computational complexity and statistical validity; • engage students with theoretical insights into Machine Learning and Deep Learning paradigms to support them to innovate at the algorithmic level and contribute to development of emerging approaches and applications; • instil in students a passion for Algorithms and Complexity as to ensure effective and efficient solutions that utilise Machine Learning and Artificial Intelligence; • cultivate students appreciation and awareness of accountability, transparency, ethics and regulatory concerns as they relate to Machine Learning and Artificial Intelligence as well as the skills to convince others of their central importance; • stimulate and cement students in the history and philosophical aspects of Machine Learning and Artificial Intelligence as to appreciate the scope and limitations of both; • produce graduates fit to occupy responsible positions in the information technology industry; • give students the opportunity to choose selected topics to study, thereby equipping the best graduates to enter research programmes; • encourage independent study habits that will stand graduates in good stead throughout their professional careers; • enable students to enhance their transferable and interpersonal skills, particularly written and oral communication and team working; • equip students with the knowledge, skills, values, and graduate attributes that will enable them to take action towards sustainable computing, spread awareness about the need for sustainable computing, and help create systemic solutions for a sustainable society; • provide opportunities for students to critically evaluate emerging knowledge and literature; • equip students with the research skills and knowledge to plan, execute, reflect and refine an effective research plan and investigation; • allow students to undertake research in a problem space of their choice, and contribute to the state of the art in Machine Learning and Artificial Intelligence; In line with curricular recommendations from bodies such as the UK's QAA and the US's Association for Computing Machinery (ACM), this programme recognises that the body of knowledge in Computing Science has grown so extensively that it is impossible to cover everything in a single programme. Instead, the Benchmark and Body of Knowledge definitions from QAA and ACM respectively define key attributes of a CS graduate, specify a small core of knowledge that all graduates should know, and accept that institutions will define specialisms that enable to graduates to study at a deep level in specific areas. These specialisms match both to areas of strength within the School of Computing Science, but are also determined in discussion with our industry partners.