Statistical Learning

About this programme

The course treats basic principles and methods of statistical learning, classification and prediction. As part of this the following concepts are studied; basics of regression and discriminant analysis, model selection and model assessment, regularization through shrinkage and smoothing, tree-based methods such as bagging, random forests and boosting, and support-vector machines for classification and regression. The course replaces the previous course with the same name and course code MT7038, and so cannot be included in the same degree as MT7038. Teaching consists of lectures, exercise sessions and supervision in computer rooms. Assessment takes place through a written exam, and written and oral presentation of the hand-in assignments. Hastie, Tibshirani & Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed). Springer. To follow your passion. To be close to international career opportunities. To explore city and countryside. In Stockholm you can really get the best of both worlds. Here we explain all the different steps you need to go through when applying to a course or programme. Stockholm University arranges regular webinars and participates in educational fairs and events virtually and around the world to meet students and inform them about our study programmes. Every year Stockholm University welcomes thousands of international students from all over the world. Each bringing their own backpack full of experiences, expectations and dreams.