MEng 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
Master of Engineering in Artificial Intelligence, a computer science (artificial intelligence) degree centred on modern approaches to artificial intelligence and machine learning. Core topics include Programming, Computer Architecture, Embedded Systems, Signal Processing, Data Analysis, Statistics, Mathematical Modelling, Research Methods, Project Management, Critical & Analytical Thinking, Artificial Intelligence & Machine Learning. The curriculum incorporates a group design project in the final year. The first year establishes fundamental technical foundations through modules such as High-Level Programming, Low-Level Programming, Digital Computer Systems, and Data Analytics. The second year progresses to advanced computing and analytical concepts with Artificial Intelligence and Learning Machines, Machine Learning (I), Digital Signal Processing, and Parallel and Distributed Computing. In the third year, students undertake an individual project alongside advanced optional choices such as Advanced Computer Architecture, Computer Vision, Deep Reinforcement Learning for Robotics, and Natural Language Processing. The final year involves collaborative intensive project work through the Group Design Project, complemented by specialised optional modules including Deep Learning Research, Intelligent Mobile Robotics, Multimodal Artificial Intelligence, and Human Robot Interaction.
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
- Group Design Project
- Industrial Studies
- Algorithmic Game Theory
- Automated Software Verification
- Bayesian, Active & Reinforcement Learning
- Biometrics
- Cryptography
- Data Economy
- Data Mining
- Data Visualisation
- Deep Learning Research
- Digital Forensics
- Evolution of Complexity
- Hardware Accelerators
- Human Robot Interaction
- Image Processing
- Individual Research Project
- Intelligent Agents
- Intelligent Mobile Robotics
- Introduction to Deep Learning (MSc)
- Introduction to Quantum Computing
- Knowledge Graphs for AI Systems
- Multimodal Artificial Intelligence
- Optimisation for Machine Learning
- Probability in Computing
- Research Topics on Interaction Science
- Simulation Modelling for Computer Science
- Social Media and Network Science
- Software Modelling Tools and Techniques for Critical Systems
- Software Project Management and Secure Development
- Software Security