Statistical Learning

 General information


Monday, 10am - 12pm, M5
Thursday, 10am - 12pm, M5


Prof. Dr. Gerold Alsmeyer


Christopher Eick


The course in the course catalogue
The tutorial in the course catalogue

Prerequisites: Probability Theory and Statistics; Probability Theory II may be beneficial

Course syllabus:

This course aims at an introduction of some basic aspects in statistical learning and data science. We plan to cover topics in stochastic approximation, pattern recognition, kernel density estimation, regression, supervised and unsupervised learning, and prediction.
Knowledge of the fundamental concepts in Mathematical Statistics, especially about estimation and testing, are expected.


The corresponding Learnweb course can be found here. The key is: StatisticalLearning

Course assessment:

There will be an oral exam/exam at the end of the course. Admission to the exam is conditional on obtaining at least 40% of the points of the problem sets.

Exam: tba



Wednesday, 10am - 12pm

Problem sets:

The problem sets can be found in the Learnweb course.