Statistical Learning

The course focuses mainly on the applied aspects of statistical learning. However, the most important basic properties of, and relations between different statistical learning models and algorithms are also included. The course covers supervised learning algorithms, with special emphasis on classification methods such as logistic regression, classification trees, linear discriminant analysis, quadratic discriminant analysis, K-nearest neighbour, support vector machine, and regression methods such as linear regression, smoothing splines, generalised additive model, and regression trees. The course also covers unsupervised learning methods such as principal component analysis, k-mean clustering, and hierarchical clustering. Model validation through cross validation, and bootstrap methods are covered. Regularisation for model selection, high dimensional data analysis, and improving prediction performance through model averaging, bagging, and boosting techniques are also covered.