STA4042 Statistical Learning: Home

This course introduces the general concepts and algorithms of statistical learning. The aim is to provide the student with a systematic way of approaching problems in data processing. At the end of the semester, the student should be able to understand the specificity of a given task (classification/regression, supervised unsupervised, dimensionality and overfitting issues), to preprocess the data, to implement the appropriate algorithm (discriminant analysis, support vector machine, regularised regression, tree-based methods, clustering methods...) and to ensure that the output is statistically relevant and robust (cross-validation, bootstrap). The course will be both theoretical, relying mainly on probabilistic and linear algebra inferences, and practical, through the implementation of algorithmic solutions to real-world problems.