Statistical Learning

Lecturer: Michael Vogt

Exercises: Manuel Rosenbaum

General Informations:

Lectures:	2 SWS: Tuesday 14:00 - 16:00; Helmholtzstraße 18, Room 120
Exercises:	1 SWS: Thursday, 12:00 - 14:00; N24, Room 226
Contents:	1. Classification and Regression Problems - Statistical decision theory - Binary classification - Logistic regression 2. Technical Tools - Exponential inequalities - Concentration inequalities - Subgaussian random variables 3. Uniform Convergence and Generalization - Classification with 0-1-loss - Convex relaxation for classification - Regression 4. Neural Networks - Definition of deep neural networks - Statistical model - Generalization bounds based on Rademacher complexity - Approximation theory - Convergence rates