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.

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