Statistical Learning
Prof. Dr. Christian Bender
Winter Semester 2024/2025
Recommended prerequisites
Knowledge of measure-theoretic probability theory at the level of the mathematics course Stochastik I.
Lectures
Thursdays, 12.15 - 13.45 pm,
building E2 4, HS IV (room 1.15)
Tutorials
One hour per week (by arrangement)
Exam
Oral exam at the end of the semester.
Course material
The course material can be found on the learning management system Moodle. If you are interested to attend the course, please ask for the enrolment key at your earliest convenience via email to:
bender [at] math.uni-sb.de
Moodle enrolment
Contents
- Introduction to the regression problem and to pattern recognition
- Local averaging methods (e.g., kernel smoothing, k-nearest neighbor)
- Concentration inequalities (Hoeffding, Bernstein)
- Sample splitting
- Empirical risk minimization
- Vapnik-Chervonenkis inequality
- Combinatorial aspects of the Vapnik-Chervonenkis theory