Machine Learning, Mathematics & Statistics
Entry requirements
A level: AAB or BBB (Additional requirements: A-level Mathematics.) IB: 34 (6,5,5) (Additional requirements: HL Mathematics (Analysis & Approaches).) Scottish Higher: BBBB is the minimum requirement from S5 to be reviewed for an S6 offer Offers are not guaranteed to applicants who meet the minimum from S5 Typically offers will be made at AAAAA by end of S6. B at Advanced Higher is equivalent to A at Higher Additional requirements: Higher Mathematics and a Higher Science subject at AA. (AB may be considered). Scottish Higher (widening access): MD20 : BBBB (also other target groups*) MD40 : AABB* Additional requirements: Higher Mathematics and a Higher Science subject. Successful completion of Top-Up or one of our Summer Schools.
About this course
BSc/MSci in Machine Learning, Mathematics & Statistics, a machine learning, mathematics & statistics degree centred on the integration of statistical theory, computational methods and mathematical principles. Core topics include Data Analysis, Statistics, Mathematical Modelling, Research Methods, and Artificial Intelligence & Machine Learning. The programme spans four to five years, culminating in independent project work. The first year introduces foundational concepts through Computing Science 1P (Standard Route), Computing Science 1F - Computing Fundamentals, Mathematics 1, and Statistics 1Y: Introduction to Statistics: Learning from Data. The second year expands into advanced mathematics and programming via Mathematics 2A: Multivariable Calculus, Mathematics 2B: Linear Algebra, Algorithms & Data Structures 2, and Statistics 2R: Probability. The third year covers core computational and statistical theory through modules such as Artificial Intelligence: Machine Learning, Artificial Intelligence: Deep Learning, Regression Models, and Bayesian Statistics. The fourth year features a Science Individual Project alongside Research Methods And Techniques (M) for MSci. The fifth year for MSci students includes a Science Individual Research Project and Project Research Readings In Computing Science (M).
Modules
- Computing Science 1P (Standard Route)
- Computing Science 1F - Computing Fundamentals
- Computing Science - 1S Systems
- Mathematics 1
- Statistics 1Y: Introduction to Statistics: Learning from Data
- Statistics 1Z: Data Modelling in Action
- Computing Science 1PX (Alternate Route)
- Computing Science - 1CT Introduction to Computational Thinking
- Artificial Intelligence 2Y: Knowledge Representation and Decision Making
- Algorithmic Foundations 2
- Algorithms & Data Structures 2
- Mathematics 2A: Multivariable Calculus
- Mathematics 2B: Linear Algebra
- Statistics 2R: Probability
- Statistics 2X: Probability II
- Statistics 2S: Statistical Methods, Models and Computing 1
- Statistics 2Y: Statistical Methods, Models and Computing 2
- Computing Science
- Artificial Intelligence: Machine Learning
- Artificial Intelligence: Deep Learning
- 4H: Theoretical Foundations of Machine Learning and Deep Learning
- Regression Models
- Generalised Linear Models
- Statistical Inference
- Bayesian Statistics
- Science Individual Project
- Research Methods And Techniques (M) for MSci
- Science Individual Research Project
- Project Research Readings In Computing Science (M)