Statistical Learning
Lecturer: Michael Vogt
Exercises: Manuel Rosenbaum
General Informations:
| Lectures: | Thursdays, 12:15-13:45, Room E20 (Heho 18) |
| Exercises: | Tuesdays, 14:15-15:15, Room 120 (Heho 18) |
| Contents: | 1. Classification and Regression Problems - Statistical decision theory - Binary classification - Logistic regression 2. Technical Tools - Exponential inequalities - Sub-Gaussian random variables - Concentration inequalities 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 |
| Moodle |