Unsupervised Learning

About this programme

The aim of the course is to introduce basic as well as modern concepts of statistical learning without training data (unsupervised statistical learning), with applications in statistical data analysis. Central concepts covered include similarity measures, linear and nonlinear methods of dimensional reduction, centroid, distributional and density-based methods of cluster analysis, hierarchical methods and different validation methods. The course replaces the previous course with the same name and course code MT7039, and so cannot be included in the same degree as MT7039. Instruction is given in the form of lectures, exercise sessions and supervision. Assessment takes place through a written exam, and home exam 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.