The MathSys MSc year is structured to provide students with the mathematical training to tackle key challenges facing science, business and society.
It is dedicated to developing a broad portfolio of mathematical techniques through taught modules covering subjects such as Stochastic Modelling and Random Processes, Networks, Numerical Algorithms, Optimization, Statistical Inference, Data Analysis, Machine Learning and Modern Topics in Mathematics Modelling. Alongside this, MSc students will also undertake projects and work on research problems that will have a strong emphasis on applied questions and practical approaches, and will be linked to real-world problems and experiences from our external collaborative partners.
Term 1 taught core modules
- MA933 Stochastic modelling and random processes (15 CATS), weeks 1-10, 3h lectures and 1h classes
- MA934 Numerical algorithms and optimization (15 CATS), weeks 1-5, 4h lectures and 4h classes
- MA930 Data Analysis and Machine Learning (15 CATS), weeks 6-10, 4h lectures and 4h classes
Term 2 taught core module
- MA999 Fundamentals of Mathematical Modelling (15 CATS), weeks 1-10
Other core modules
- MA932 MSc Study Groups (40 CATS), begins in term 2 and runs mainly in Easter vacation and term 3
- MA931 MSc Project (50 CATS), runs from mid June to September
Other compulsory activities
- MSc student meetings (usually Wednesdays 10-11) with administrator and MSc coordinator to discuss general questions (e.g. module registrations etc)
- Computational Techniques (term 1 Wednesdays 11-12)
- Complexity/MathSys Forum (usually Wednesdays 1-2): seminar series of the centre preceded by a joint sandwich lunch prepared by MSc students in groups
- Transferable skills activities
- Attendance at annual retreat (x 3 nights residential event held in UK), summer school, and other events/activities as requested by the CDT and outlined in the Terms and Conditions (for funded students) and the Student Handbook
Taught optional modules
Most of these modules run in term 2, please check up to date timetables with individual departments. You should take at least 2 options summing to at least 30 CATS.
CS404 - 15 Agent Based Systems
CS409 - 15 Algorithmic Game Theory
CS904 -15 Computational Biology
CS909 - 15 Data Mining
IM903 - 15 Complexity in the Social Sciences (runs as a one week module after the end of term 2)
IM931 - 15 Interdisciplinary Approaches to Machine Learning
MA4E7 - 15 Population Dynamics: Ecology and Epidemiology
MA5Q3 - 18 Topics in Complexity Science: TBA [not running in 2019/20]
You can also choose other Masters level modules subject to approval of the course director as unusual options, a list of examples can be found here.