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.

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