This lecture covers selected advanced topics in computational statistics, including various classification methods, the EM algorithm, clustering, handling missing data, and graphical modelling.
Lernziel
Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.
Inhalt
The course is roughly divided in three parts: (1) Supervised learning via (variations of) nearest neighbor methods, (2) the EM algorithm and clustering, (3) handling missing data and graphical models.
Skript
Lecture notes.
Voraussetzungen / Besonderes
We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.