- 30 hours of lectures + 10 hours tutorials via office hours
- Statistical Learning
- An introduction to statistical learning theory: from over-fitting to apparently complex methods which can work well. For example, VC dimension and shattering sets, PAC bounds.
- Loss functions. Risk (in the learning theory sense); posterior expected risk. Generalisation error.
- Supervised, unsupervised and semi-supervised learning.
- The use of distinct training, test and validation sets particularly in the context of prediction problems.
- The bootstrap revisited. Bags of little bootstraps. Bootstrap aggregation. Boosting.
- ML method will be used to illustrate these ideas.
- Big data and big model related issues and (partial) solutions
- "Curse of dimensionality". Multiple testing: voodoo correlations; false-discovery rate and family-wise error rate; corrections: Bonferroni, Benjamini-Hochberg.
- Sparsity and regularisation. Variable selection; regression. Spike and slab priors. Ridge regression. The Lasso. The Dantzig selector.
- Concentration of measure, related inferential issues.
- MCMC in high dimensions, preconditioned Crank Nicolson; MALA; HMC. Preconditioning. Rates of convergence.
- 1 x 2-hour exam
- Chris Bishop, Pattern recognition and machine learning, Springer, 2006.
- Peter Buehlman, Statistics for high-dimensional data: methods, theory and applications, Springer, 2011.
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The elements of statistical learning, Springer, 2009.
- Trevor Hastie, Robert Tibshirani and Martin Wainwright, Statistical learning with sparsity, CRC Press, 2015.
- Kevin Murphy, Probabilistic machine learning: a probabilistic perspective, MIT Press, 2012.
Examination Period: April