Methodological training
This site is a long collection of buzzwords about some of the techniques we cover in the course through our methodological training. The exact content does vary from year to year and depends on the (term II) modules chosen, but this list at least provides you with an idea of the scope of the overall programme.
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Programming techniques Performance analysis tools Explicit vectorisation Scalability models OpenMP programming MPI programming |
Principles and techniques in data analysis Inference and learning Bayesian statistics Simple models Graphical models Monte Carlo methods |
| Unsupervised Learning Dimension reduction, PCA Mixture models Kernel methods |
Equations and numerical methods ODE discretisation methods Finite difference models Stability analysis |
| Regression Linear regression Regularization, Lasso, sparsity Smoothing, kernels, and splines Gaussian processes |
Algorithms SAT algorithms Integer optimisation Complexity measurements Stochastic models |
| Classification Linear classifiers Generalized linear models Kernels and SVMs Boosting, bagging, decision trees, random forests Neural networks and deep learning |