Unsupervised Learning
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.
The course consists of two modules, theory and hand-in assignments.
Teaching Format
Instruction is given in the form of lectures, exercise sessions and supervision.
Assessment
Assessment takes place through a written exam, and home exam of the hand-in assignments.
Examiner
A list of examiners can be found on
Hastie, Tibshirani & Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed). Springer.
Bishop: Pattern Recognition and Machine Learning. Springer.
Lee & Verleysen: Nonlinear Dimensionality Reduction. Springer.
Articles.
