BSc Mathematics, Artificial Intelligence, and Real-world Systems (MARS)
Entry requirements
A level: AAA. This should include Mathematics grade A or Further Mathematics grade A. The overall offer grades will be lowered to AAB for applicants who achieve both Mathematics and Further Mathematics at grades AB, in either order. IB: 36 points overall with 16 points from the best 3 HL subjects including 6 in Mathematics HL (either analysis and approaches or applications and interpretations)
About this course
BSc Hons in Mathematics, Artificial Intelligence, and Real-world Systems (MARS), a mathematics and computer science (artificial intelligence) degree centered on mathematical and computational approaches to modern systems and applications. Core topics include Programming, Signal Processing, Data Analysis, Statistics, Mathematical Modelling, Fieldwork, Environmental Analysis, Human Behaviour, and Artificial Intelligence & Machine Learning. The curriculum features an industry-inspired project in the final year. The first year covers core mathematical foundations through modules such as Logic and Discrete Mathematics, Mathematical Modelling and Programming, Matrices and Calculus, Multivariate Calculus, Probability and Statistics, and Symmetry and Sequences. The second year advances core knowledge via Linear Algebra, Mathematics of Artificial Intelligence, Multivariate Probability and Statistics, and Real-world Dynamics, alongside optional subjects like Applied Data Science, Atmospheric Science, Complex Analysis, Ecology and Conservation, Electromagnetism and Communications, Fluid Mechanics and Mass Transfer, Glaciology, Real Analysis, and Soil Science. The third year centres on an Industry-inspired Project, supported by optional modules including Advanced Differential Equations, Dynamic Modelling, Environmental Statistics, Graph Theory and Algorithms, Linear Systems, Mathematical Cryptography, Mathematics of Generative Modelling, Medical Statistics, Metric Spaces and Topology, Nonlinear Systems and Chaos, Optimisation for Machine Learning, Statistical Inference, Statistical Learning and Prediction, and Stochastic Processes.
Modules
- Logic and Discrete Mathematics
- Mathematical Modelling and Programming
- Matrices and Calculus
- Multivariate Calculus
- Probability and Statistics
- Symmetry and Sequences
- Linear Algebra
- Mathematics of Artificial Intelligence
- Multivariate Probability and Statistics
- Real-world Dynamics
- Applied Data Science
- Atmospheric Science
- Complex Analysis
- Ecology and Conservation
- Electromagnetism and Communications
- Fluid Mechanics and Mass Transfer
- Glaciology
- Real Analysis
- Soil Science
- Industry-inspired Project
- Advanced Differential Equations
- Dynamic Modelling
- Environmental Statistics
- Graph Theory and Algorithms
- Linear Systems
- Mathematical Cryptography
- Mathematics of Generative Modelling
- Medical Statistics
- Metric Spaces and Topology
- Nonlinear Systems and Chaos
- Optimisation for Machine Learning
- Statistical Inference
- Statistical Learning and Prediction
- Stochastic Processes