University of Hull

What you'll study

The programme combines a solid grounding in computer science with specialist AI modules. Early years cover fundamental topics such as programming, data structures and algorithms, discrete mathematics and software engineering, while later years introduce machine learning, deep learning, natural language processing, computer vision, knowledge representation and robotics. Practical, project-based learning is emphasised throughout: you will complete laboratory exercises, group design projects and an individual final-year project that applies AI methods to a real-world problem.

Typical modules or subject areas include:

• Foundations of Programming and Software Development
• Algorithms, Data Structures and Computational Thinking
• Mathematics for Computer Science (linear algebra, probability and statistics)
• Databases and Data Management
• Machine Learning and Statistical Learning Methods
• Deep Learning and Neural Networks
• Natural Language Processing and Speech Technologies
• Computer Vision and Image Analysis
• Robotics, Sensors and Autonomous Systems
• Ethics, Safety and Social Implications of AI
• Group Design Projects and Industry-Facing Case Studies
• Individual Honours Project (advanced research or development project)

The BSc route is typically delivered over three years full-time, while the MEng is an integrated master’s route over four years that includes more advanced technical modules and a major project. Many students have the option to take a placement (sandwich) year or shorter industry experience modules to gain practical experience.

Entry requirements

Entry expectations reflect the technical nature of the course. Applicants are generally expected to demonstrate competence in mathematics and some programming experience or a related qualification. Typical offers consider A-levels, Scottish Highers, BTEC or equivalent qualifications, and international equivalents; entry decisions may also take into account UCAS tariff points and personal statements.

Acceptable preparation can include:

• A-levels including Mathematics, or an equivalent qualification demonstrating quantitative ability
• A background in computing, engineering or a STEM subject is advantageous
• Demonstrable programming experience (self-study, school, or work) is helpful
• Applicants without a traditional background may be considered via foundation year routes or relevant experience

The university also considers mature applicants and those with vocational qualifications; you should consult the University of Hull admissions pages for the specific entry profile that applies to your circumstances.

Career prospects

Graduates move into a wide range of technical and professional roles. Typical destinations include positions as machine learning engineers, data scientists, AI developers, software engineers, computer vision specialists, robotics engineers and applied research assistants. Employers span sectors such as technology, finance, healthcare, automotive and manufacturing, as well as public sector organisations and consultancies.

Graduates also use the degree as a platform for further study — pursuing MSc programmes in advanced AI, data science or research degrees (PhD) — or for professional conversion into roles such as product management and technical consultancy. Practical projects, placement opportunities and the final-year project help build a portfolio of work that supports job applications.

Why study at University of Hull

The University of Hull offers a focused computing environment with hands-on teaching in modern labs and access to research-active staff working in areas of relevance to AI. The course emphasises practical skills, industry-relevant tools and software, and opportunities for work experience through placement routes or employer-linked projects.

Support services include dedicated careers and employability teams, project supervision from academics, and extracurricular activities such as coding clubs and hackathons. Hull’s campus provides a supportive student community and accessible facilities for collaborative study, making it a practical choice for students aiming to build both technical competence and real-world experience in artificial intelligence.