BSc Mathematics, Artificial Intelligence, and Real-world Systems (MARS) (Placement Year)
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) (Placement Year), a mathematics, computer science and computer science (artificial intelligence) degree centred on mathematical and computational approaches to physical theory. Core topics include Programming, Signal Processing, Data Analysis, Statistics, Mathematical Modelling, Fieldwork, Environmental Analysis, Human Behaviour, Artificial Intelligence & Machine Learning. The programme includes a full-year professional placement role in industry during the third year, alongside project work inspired by industry partners. The first year covers foundational concepts through compulsory modules such as Logic and Discrete Mathematics, Mathematical Modelling and Programming, and Matrices and Calculus. The second year advances core knowledge with Linear Algebra, Mathematics of Artificial Intelligence, and Real-world Dynamics, while offering optional modules across subjects like Applied Data Science, Atmospheric Science, and Ecology and Conservation. Following the third-year placement, the final year features the Industry-inspired Project, alongside optional choices such as Advanced Differential Equations, Dynamic Modelling, and Statistical Learning and Prediction.
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
- Placement
- Industry-inspired Project
- Advanced Differential Equations
- Dynamic Modelling
- Environmental Statistics
- Graph Theory and Algorithms
- Linear Systems
- Mathematics of Generative Modelling
- Metric Spaces and Topology
- Nonlinear Systems and Chaos
- Statistical Inference
- Statistical Learning and Prediction
- Mathematical Cryptography
- Stochastic Processes
- Medical Statistics
- Optimisation for Machine Learning