Click here for general information about course choices.
Courses on this site are structured by the years they are typically taken in. However, if a course is normally taken in a later (or earlier) year, it does not necessarily mean that you cannot take it now.
Course level is generally a good indicator of course difficulty. Courses of level up to and including 9 are normally taken in years 1 and 2, while level 10 and 11 courses are typically taken in years 3 through 5. If you are on an MMath degree, you are required to take a total of 120 credits of level 11 courses over the years 4 and 5, the 40-credit dissertation in year 5 counts towards this total.
Please don't hesitate to contact your P.T. or the course organizer of the course if you have special requirements or are overwhelmed. For example, if there is a specific course that you would like to take outside of the typical regime, do not be afraid to ask these people for advice and/or a concession for the course.
Note that while the school advises against overloading on course credits, there is nothing actually stopping you from doing this. However, with an increased load your academic performance may suffer. Quite often, 20 credit and 10 credit courses require a similar amount of effort to do well in.
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General info Edit on GitHub
Advanced Methods of Applied Mathematics April/May exam Edit on GitHub
Relevant reading available online
- G.F. Carrier, M. Krook and C.E. Pearson, Functions of a Complex Variable, McGraw-Hill, New York (1966).
- G.B. Whitham, Linear and Nonlinear Waves, J. Wiley and Sons, New York (1974).
Applied Stochastic Differential Equations December exam Edit on GitHub
Relevant reading available online
- G.A. Pavliotis, Stochastic Processes and Applications, Springer (2014)
Differentiable Manifolds December exam Edit on GitHub
Resources
- Formula Sheet from Will
Relevant reading available online
- John Lee, Introduction to smooth manifolds, Springer 2012
- Loring Tu, Introduction to Manifolds, Springer 2010
Entrepreneurship in the Mathematical Sciences Edit on GitHub
Relevant reading available online
- Francis Greene, Entrepreneurship: Theory and Practice, forthcoming, Palgrave.
- Avner Friedman and Walter Littman, Industrial Mathematics: A Course in Solving Real-World Problems, SIAM, 1994.
- Glenn Fulford and Philip Broadbridge, Industrial Mathematics: Case Studies in the Diffusion of Heat and Matter, Cambridge University Press, 2010.
Essentials in Analysis and Probability December exam Edit on GitHub
Resources
- Overview Sheet from Sebastian (Source on GitHub
Relevant reading available online
- R. M. Dudley, Real Analysis and Probability, Cambridge University Press, 2004.
Fundamentals of Operational Research December exam Edit on GitHub
General Topology Edit on GitHub
Group Theory April/May exam Edit on GitHub
Resources
- Summary from Will
Introduction to Partial Differential Equations December exam Edit on GitHub
Resources
- Notes from Owen
Linear Analysis April/May exam Edit on GitHub
Relevant reading available online
- An Introduction of Hilbert Space, by N. Young, Cambridge Mathematical Textbooks.
Mathematical Biology December exam Edit on GitHub
Relevant reading available online
- J.D. Murray. Mathematical Biology I: An Introduction. (Interdisciplinary Applied Mathematics.) Springer-Verlag, 2007.
- L. Edelstein-Keshet. Mathematical Models in Biology. (Classics in Applied Mathematics.) Society for Industrial and Applied Mathematics, 2005.
- F. Brauer and C. Castillo-Chavez. Mathematical Models in Population Biology and Epidemiology. (Texts in Applied Mathematics.) Springer-Verlag, 2012.
Mathematical Education Edit on GitHub
Mathematical Project (Single) Edit on GitHub
Probability, Measure & Finance April/May exam Edit on GitHub
Project in Mathematics (Double) Edit on GitHub
Statistical Case Studies Edit on GitHub
Relevant reading available online
- Faraway, J.J., 2016. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC press.
- James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An introduction to statistical learning (Vol. 112, p. 18). New York: Springer.
- Wood, S.N., 2017. Generalized additive models: an introduction with R. CRC press. 2nd edition.