Machine learning and AI structure University of London
Develop your personal and professional skills and reflect on these during your placement. Get an overview of Android app development, native apps and app wrappers, patterns, and the terminology of Domain-Driven Design. Get an overview of how technology is used, artificial intelligence engineer degree deployed and developed in a business environment. Develop soft and transferable skills to help you collaborate successfully on a team project. You’ll review some basic mathematical concepts important for computer science, but you’ll mainly do practical work.
Based in London, renowned for AI breakthroughs like AlphaGo, the programme emphasises multidisciplinary learning. Students collaborate on projects with stakeholders to solve sustainability challenges using AI, such as delivering medical supplies by drones or optimising agricultural practices. Ideal for graduates in computer science, mathematics, or related fields, the programme combines technical expertise with practical problem-solving, bridging AI innovation and global well-being. UCL’s strong AI reputation and global partnerships enrich the learning experience, shaping the next generation of AI specialists for a positive global impact. Course delivery is a combination of theory, practical sessions and project work. In your first two years, you will gain solid knowledge and understanding of electronic engineering.
The artificial intelligence industry is fast-growing and touches so many aspects of society, from business operations to our everyday lives. Alongside the subject-specific knowledge you will gain during the programme, you will also develop professional skills such as communication, teamwork, critical thinking and research. These will enhance your CV, allowing you to improve your career prospects and access more senior roles. If you meet the academic requirements but require additional support to take your language skills to the required level, you may be able to attend one of our pre-sessional English courses.
In addition, a candidate must reach the appropriate level of English requirement for the particular course. For study on our Foundation and Undergraduate programmes, English language at grade C or above in the Kenya Certificate of Secondary Education (KCSE) is sufficient to meet the standard English language requirements. For study on our Foundation and Undergraduate programmes, https://www.metadialog.com/ English language at grade C or above (or in numerical terms, grade 6 or above) in the WAEC SSCE is sufficient to meet the standard English language requirements. Students who hold a Higher National Diploma with a good profile of grades (distinctions and credits, or grades 1-3) will be considered for entrance to undergraduate programmes (first year entry).
The library staff offer additional support to students, including help with academic writing, research strategies, literature searching, reference management and assistive technology. There is also a ‘Just Ask’ service for help and advice, live LibChat, online workshops, tutorials and drop-ins available from our Learning Services, and weekly library live chat sessions that give you the chance to ask the library teams for help. We will support you to confidently use a huge range of learning technologies, including Blackboard, artificial intelligence engineer degree Collaborate Ultra, DMU Replay, MS Teams, Turnitin and more. Alongside this, you can access LinkedIn Learning and learn how to use Microsoft 365, and study support software such as mind mapping and note-taking through our new Digital Student Skills Hub. You will acquire new skills and knowledge, all while continuing to work in your current role. You study at City Campus through a structured mix of lectures, seminars and practical sessions as well as access to digital and online resources to support your learning.
Is it hard to be an AI engineer?
AI engineering can be challenging to study due to its multidisciplinary nature, which combines concepts from computer science, mathematics, statistics, and domain-specific knowledge. It requires a solid foundation in programming, algorithms, machine learning, and deep learning.