BEng Artificial Intelligence
Entry requirements
A level: A*AA including mathematics (minimum grade A). IB: Pass, with 38 points overall, with 19 points required at Higher Level including 6 at Higher Level in Mathematics (Analysis and Approaches) or 7 at Higher Level in Mathematics (Applications and Interpretation)
About this course
Bachelor of Engineering in Artificial Intelligence, a computer science (artificial intelligence) degree focused on the principles and applications of machine learning and intelligent systems. Core topics include Programming, Computer Architecture, Embedded Systems, Signal Processing, Data Analysis, Statistics, Mathematical Modelling, and Artificial Intelligence & Machine Learning. The curriculum includes practical lab work, interdisciplinary group projects, and an individual research project in the final year. The first year establishes core foundations through modules such as High-Level Programming, Low-Level Programming, Digital Computer Systems, and Algorithms and Analysis. The second year progresses to advanced computing and analytical techniques via Artificial Intelligence and Learning Machines, Machine Learning (I), Digital Signal Processing, and Parallel and Distributed Computing. The third year centres on independent research through Part III Individual Project Phase 1 and Part III Individual Project Phase 2, alongside advanced mathematical foundations in Mathematics of Machine Learning. Students can further tailor their studies by choosing from optional modules spanning Advanced Computer Architecture, Advanced Computer Networks, Advanced Databases, Biosensors and Diagnostics, Causal Reasoning and Machine Learning, Cloud Application Development, Computational Biology, Computer Vision, Deep Reinforcement Learning for Robotics, High Performance Computing, History of Computing, Imaging for Digital Health and Bioscience, Interaction Science, Introduction to Bionanotechnology and Computational Biology, Natural Language Processing, Precision Health: Machine Learning Under Uncertainty, Real-Time Computing and Embedded Systems, Robot Kinematics and Dynamics, Security of Cyber Physical Systems, Social Computing Techniques, and Web and Cloud Based Security.
Modules
- AICE Lab Programme Year 1
- Algorithms and Analysis
- Data Analytics
- Digital Computer Systems
- Ethics and Security of Computing
- High-Level Programming
- Low-Level Programming
- Mathematics for Artificial Intelligence and Computer Engineering (I)
- Mathematics for Artificial Intelligence and Computer Engineering (II)
- AI and CE Interdisciplinary Group Project
- Artificial Intelligence and Learning Machines
- Code Transformation
- Digital Signal Processing
- Machine Learning (I)
- Parallel and Distributed Computing
- Scientific Computing
- Systematic Design
- Mathematics of Machine Learning
- Part III Individual Project Phase 1
- Part III Individual Project Phase 2
- Advanced Computer Architecture
- Advanced Computer Networks
- Advanced Databases
- Biosensors and Diagnostics
- Causal Reasoning and Machine Learning
- Cloud Application Development
- Computational Biology
- Computer Vision
- Deep Reinforcement Learning for Robotics
- High Performance Computing
- History of Computing
- Imaging for Digital Health and Bioscience
- Interaction Science
- Introduction to Bionanotechnology and Computational Biology
- Natural Language Processing
- Precision Health: Machine Learning Under Uncertainty
- Real-Time Computing and Embedded Systems
- Robot Kinematics and Dynamics
- Security of Cyber Physical Systems
- Social Computing Techniques
- Web and Cloud Based Security