| || |
The aim of this module is to introduce the range of applied mathematical and statistical techniques needed to understand data science algorithms, forming a foundation for PhD research on the development of new methods. Syllabus: 1. Mathematical foundations: multivariate calculus, linear algebra, probability theory, 2. Mathematical modelling: asymptotic analysis, uncertainty quantification, 3. Networks, 4. Optimisation, 5. Computational programming, 6. Statistical inference, 7. Statistical computing.