Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.
Advanced Topics in Computational Statistics
|Semester||Autumn Semester 2016|
|Lecturers||M. H. Maathuis|
|Periodicity||two-yearly recurring course|
|Course||Does not take place this semester.|
|Language of instruction||English|
|Abstract||This lecture covers selected advanced topics in computational statistics, including various classification methods, the EM algorithm, clustering, handling missing data, and graphical modelling.|
|Objective||Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.|
|Content||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.|
|Lecture notes||Lecture notes.|
|Prerequisites / Notice||We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.|